{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for plotting in notebook\n",
    "%matplotlib inline    \n",
    "\n",
    "# load Python packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as sm    # for regressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 2a: Spike in mobility around strikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in drone strike data\n",
    "# subset of 74 strikes was compiled from the Bureau of Investigative Journalism and the New America think tank\n",
    "# see paper for details\n",
    "strikes = pd.read_csv('data_general/drone_strike_data.csv', engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load mobility data\n",
    "mobility = pd.read_csv('data_mobility/mobility_daily.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strike</th>\n",
       "      <th>day</th>\n",
       "      <th>id</th>\n",
       "      <th>mobility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>15700</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>113695</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>209385</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>362258</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>546570</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strike  day      id  mobility\n",
       "0       1   -7   15700       0.0\n",
       "1       1   -7  113695       0.0\n",
       "2       1   -7  209385       0.0\n",
       "3       1   -7  362258       0.0\n",
       "4       1   -7  546570       0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the mobility dataframe records the distance travelled by proximal individuals\n",
    "# each day for 7 days before the strike, on the day of the strike, and 21 days after\n",
    "# the strike column records the strike ID, the day column records the number of days to the strike,\n",
    "# the id column records a unique anonymized ID for each individual, and\n",
    "# the mobility column records the daily distance travelled in miles\n",
    "mobility.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique individuals in the mobility data: 93429\n"
     ]
    }
   ],
   "source": [
    "# Number of unique individuals in the mobility data\n",
    "print('Number of unique individuals in the mobility data:', mobility['id'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prep mobility data for event study regression\n",
    "\n",
    "# shift the day index to only include positive values for the regression\n",
    "shift = 8\n",
    "mobility['day'] = mobility['day'] + shift\n",
    "\n",
    "# add lead/lag indicator variables corresponding to the day index\n",
    "xs = pd.get_dummies(mobility['day'], prefix='X')\n",
    "df = mobility.merge(xs, left_index=True, right_index=True)\n",
    "\n",
    "# Ensure X columns are numeric, not boolean\n",
    "for col in df.columns:\n",
    "    if col.startswith(\"X_\"):\n",
    "        df[col] = df[col].astype(int)\n",
    "\n",
    "# convert mobility in miles to kilometers\n",
    "km_scalar = 1.60934    # scalar to convert miles to kilometers\n",
    "df['mobility'] = df['mobility'] * km_scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_1 + X_2 + X_3 + X_4 + X_5 + X_7 + X_8 + X_9 + X_10 + X_11 + X_12 + X_13 + X_14 + X_15 + X_16 + X_17 + X_18 + X_19 + X_20 + X_21 + X_22 + X_23 + X_24 + X_25 + X_26 + X_27 + X_28 + X_29\n"
     ]
    }
   ],
   "source": [
    "# create string that includes all the lead/lag indicator variables\n",
    "# this string will be used in the statsmodel regression formula\n",
    "# drop the -2 day variable to avoid multicollinearity\n",
    "\n",
    "var = []\n",
    "for i in range(-7, 21+1):\n",
    "    if i == -2:\n",
    "        continue\n",
    "    var.append('X_' + str(i+shift))\n",
    "var_form = ' + '.join(var)\n",
    "\n",
    "print(var_form)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.024</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.024</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 20 Aug 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:52:48</td>     <th>  Log-Likelihood:    </th> <td>-1.9537e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>3380634</td>     <th>  AIC:               </th>  <td>3.907e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>3380532</td>     <th>  BIC:               </th>  <td>3.908e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   101</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[1]</th>   <td>   22.6216</td> <td>    0.454</td> <td>   49.791</td> <td> 0.000</td> <td>   21.731</td> <td>   23.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[3]</th>   <td>   23.7201</td> <td>    0.465</td> <td>   51.046</td> <td> 0.000</td> <td>   22.809</td> <td>   24.631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[4]</th>   <td>   29.1651</td> <td>    0.458</td> <td>   63.666</td> <td> 0.000</td> <td>   28.267</td> <td>   30.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[5]</th>   <td>   28.2505</td> <td>    0.463</td> <td>   60.966</td> <td> 0.000</td> <td>   27.342</td> <td>   29.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[6]</th>   <td>   31.4648</td> <td>    0.461</td> <td>   68.229</td> <td> 0.000</td> <td>   30.561</td> <td>   32.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[7]</th>   <td>   26.7052</td> <td>    0.460</td> <td>   58.049</td> <td> 0.000</td> <td>   25.804</td> <td>   27.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[8]</th>   <td>   26.3013</td> <td>    0.462</td> <td>   56.876</td> <td> 0.000</td> <td>   25.395</td> <td>   27.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[9]</th>   <td>   17.7873</td> <td>    0.459</td> <td>   38.746</td> <td> 0.000</td> <td>   16.888</td> <td>   18.687</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[10]</th>  <td>   21.8015</td> <td>    0.461</td> <td>   47.304</td> <td> 0.000</td> <td>   20.898</td> <td>   22.705</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[11]</th>  <td>   30.5712</td> <td>    0.461</td> <td>   66.358</td> <td> 0.000</td> <td>   29.668</td> <td>   31.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[12]</th>  <td>   21.2926</td> <td>    0.444</td> <td>   47.979</td> <td> 0.000</td> <td>   20.423</td> <td>   22.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[14]</th>  <td>   15.3721</td> <td>    0.468</td> <td>   32.821</td> <td> 0.000</td> <td>   14.454</td> <td>   16.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[15]</th>  <td>   26.9420</td> <td>    0.460</td> <td>   58.541</td> <td> 0.000</td> <td>   26.040</td> <td>   27.844</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[17]</th>  <td>   22.3700</td> <td>    0.462</td> <td>   48.427</td> <td> 0.000</td> <td>   21.465</td> <td>   23.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[19]</th>  <td>   21.2447</td> <td>    0.462</td> <td>   45.966</td> <td> 0.000</td> <td>   20.339</td> <td>   22.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[20]</th>  <td>   22.1546</td> <td>    0.458</td> <td>   48.374</td> <td> 0.000</td> <td>   21.257</td> <td>   23.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[24]</th>  <td>   39.7041</td> <td>    0.465</td> <td>   85.294</td> <td> 0.000</td> <td>   38.792</td> <td>   40.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[26]</th>  <td>   23.2795</td> <td>    0.435</td> <td>   53.539</td> <td> 0.000</td> <td>   22.427</td> <td>   24.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[27]</th>  <td>   21.5946</td> <td>    0.466</td> <td>   46.308</td> <td> 0.000</td> <td>   20.681</td> <td>   22.509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[28]</th>  <td>   22.7090</td> <td>    0.465</td> <td>   48.825</td> <td> 0.000</td> <td>   21.797</td> <td>   23.621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[31]</th>  <td>   17.3884</td> <td>    0.462</td> <td>   37.664</td> <td> 0.000</td> <td>   16.484</td> <td>   18.293</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[32]</th>  <td>   31.0847</td> <td>    0.460</td> <td>   67.563</td> <td> 0.000</td> <td>   30.183</td> <td>   31.986</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[33]</th>  <td>   23.1976</td> <td>    0.461</td> <td>   50.288</td> <td> 0.000</td> <td>   22.294</td> <td>   24.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[34]</th>  <td>   16.0935</td> <td>    0.457</td> <td>   35.191</td> <td> 0.000</td> <td>   15.197</td> <td>   16.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[35]</th>  <td>   16.0875</td> <td>    0.462</td> <td>   34.854</td> <td> 0.000</td> <td>   15.183</td> <td>   16.992</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[36]</th>  <td>   23.1653</td> <td>    0.462</td> <td>   50.138</td> <td> 0.000</td> <td>   22.260</td> <td>   24.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[37]</th>  <td>   18.1846</td> <td>    0.461</td> <td>   39.449</td> <td> 0.000</td> <td>   17.281</td> <td>   19.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[38]</th>  <td>   15.2232</td> <td>    0.458</td> <td>   33.257</td> <td> 0.000</td> <td>   14.326</td> <td>   16.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[39]</th>  <td>   21.4850</td> <td>    0.462</td> <td>   46.532</td> <td> 0.000</td> <td>   20.580</td> <td>   22.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[40]</th>  <td>   18.9805</td> <td>    0.462</td> <td>   41.040</td> <td> 0.000</td> <td>   18.074</td> <td>   19.887</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[41]</th>  <td>   18.9145</td> <td>    0.461</td> <td>   40.986</td> <td> 0.000</td> <td>   18.010</td> <td>   19.819</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[42]</th>  <td>   17.3487</td> <td>    0.462</td> <td>   37.553</td> <td> 0.000</td> <td>   16.443</td> <td>   18.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[43]</th>  <td>   24.7589</td> <td>    0.462</td> <td>   53.540</td> <td> 0.000</td> <td>   23.853</td> <td>   25.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[45]</th>  <td>   59.9838</td> <td>    0.466</td> <td>  128.703</td> <td> 0.000</td> <td>   59.070</td> <td>   60.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[47]</th>  <td>   34.2409</td> <td>    0.457</td> <td>   74.959</td> <td> 0.000</td> <td>   33.346</td> <td>   35.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[48]</th>  <td>   18.8975</td> <td>    0.459</td> <td>   41.170</td> <td> 0.000</td> <td>   17.998</td> <td>   19.797</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[49]</th>  <td>   30.6192</td> <td>    0.460</td> <td>   66.514</td> <td> 0.000</td> <td>   29.717</td> <td>   31.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[50]</th>  <td>   32.7739</td> <td>    0.461</td> <td>   71.036</td> <td> 0.000</td> <td>   31.870</td> <td>   33.678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[51]</th>  <td>   42.6047</td> <td>    0.463</td> <td>   91.957</td> <td> 0.000</td> <td>   41.697</td> <td>   43.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[54]</th>  <td>   27.3064</td> <td>    0.462</td> <td>   59.042</td> <td> 0.000</td> <td>   26.400</td> <td>   28.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[56]</th>  <td>   24.2100</td> <td>    0.461</td> <td>   52.527</td> <td> 0.000</td> <td>   23.307</td> <td>   25.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[57]</th>  <td>   31.3027</td> <td>    0.461</td> <td>   67.925</td> <td> 0.000</td> <td>   30.399</td> <td>   32.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[58]</th>  <td>   26.1722</td> <td>    0.458</td> <td>   57.084</td> <td> 0.000</td> <td>   25.274</td> <td>   27.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[59]</th>  <td>   18.2384</td> <td>    0.463</td> <td>   39.423</td> <td> 0.000</td> <td>   17.332</td> <td>   19.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[60]</th>  <td>   28.5808</td> <td>    0.452</td> <td>   63.203</td> <td> 0.000</td> <td>   27.694</td> <td>   29.467</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[61]</th>  <td>   22.3607</td> <td>    0.450</td> <td>   49.683</td> <td> 0.000</td> <td>   21.479</td> <td>   23.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[62]</th>  <td>   27.2163</td> <td>    0.462</td> <td>   58.897</td> <td> 0.000</td> <td>   26.311</td> <td>   28.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[64]</th>  <td>   32.8167</td> <td>    0.461</td> <td>   71.161</td> <td> 0.000</td> <td>   31.913</td> <td>   33.721</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[65]</th>  <td>   40.9012</td> <td>    0.457</td> <td>   89.436</td> <td> 0.000</td> <td>   40.005</td> <td>   41.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[67]</th>  <td>   29.6181</td> <td>    0.448</td> <td>   66.148</td> <td> 0.000</td> <td>   28.741</td> <td>   30.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[68]</th>  <td>   27.8218</td> <td>    0.445</td> <td>   62.460</td> <td> 0.000</td> <td>   26.949</td> <td>   28.695</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[69]</th>  <td>   33.2619</td> <td>    0.458</td> <td>   72.570</td> <td> 0.000</td> <td>   32.364</td> <td>   34.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[70]</th>  <td>   23.8595</td> <td>    0.466</td> <td>   51.147</td> <td> 0.000</td> <td>   22.945</td> <td>   24.774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[71]</th>  <td>   32.2812</td> <td>    0.458</td> <td>   70.498</td> <td> 0.000</td> <td>   31.384</td> <td>   33.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[72]</th>  <td>   34.3652</td> <td>    0.461</td> <td>   74.517</td> <td> 0.000</td> <td>   33.461</td> <td>   35.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[75]</th>  <td>   39.9147</td> <td>    0.462</td> <td>   86.360</td> <td> 0.000</td> <td>   39.009</td> <td>   40.821</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[76]</th>  <td>   62.2330</td> <td>    0.463</td> <td>  134.494</td> <td> 0.000</td> <td>   61.326</td> <td>   63.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[77]</th>  <td>   31.1554</td> <td>    0.462</td> <td>   67.383</td> <td> 0.000</td> <td>   30.249</td> <td>   32.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[78]</th>  <td>   27.2946</td> <td>    0.464</td> <td>   58.848</td> <td> 0.000</td> <td>   26.386</td> <td>   28.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[81]</th>  <td>   39.2854</td> <td>    0.456</td> <td>   86.132</td> <td> 0.000</td> <td>   38.391</td> <td>   40.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[82]</th>  <td>   27.1925</td> <td>    0.459</td> <td>   59.215</td> <td> 0.000</td> <td>   26.292</td> <td>   28.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[83]</th>  <td>   27.6238</td> <td>    0.458</td> <td>   60.319</td> <td> 0.000</td> <td>   26.726</td> <td>   28.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[85]</th>  <td>   26.2821</td> <td>    0.457</td> <td>   57.529</td> <td> 0.000</td> <td>   25.387</td> <td>   27.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[87]</th>  <td>   20.0700</td> <td>    0.461</td> <td>   43.498</td> <td> 0.000</td> <td>   19.166</td> <td>   20.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[90]</th>  <td>   14.8231</td> <td>    0.459</td> <td>   32.264</td> <td> 0.000</td> <td>   13.923</td> <td>   15.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[92]</th>  <td>   15.5966</td> <td>    0.461</td> <td>   33.855</td> <td> 0.000</td> <td>   14.694</td> <td>   16.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[95]</th>  <td>   19.2778</td> <td>    0.468</td> <td>   41.200</td> <td> 0.000</td> <td>   18.361</td> <td>   20.195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[96]</th>  <td>   40.2916</td> <td>    0.462</td> <td>   87.303</td> <td> 0.000</td> <td>   39.387</td> <td>   41.196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[97]</th>  <td>   14.0519</td> <td>    0.462</td> <td>   30.387</td> <td> 0.000</td> <td>   13.146</td> <td>   14.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[100]</th> <td>   29.3106</td> <td>    0.460</td> <td>   63.781</td> <td> 0.000</td> <td>   28.410</td> <td>   30.211</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[101]</th> <td>   44.1617</td> <td>    0.460</td> <td>   96.099</td> <td> 0.000</td> <td>   43.261</td> <td>   45.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[102]</th> <td>   34.2295</td> <td>    0.461</td> <td>   74.291</td> <td> 0.000</td> <td>   33.326</td> <td>   35.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[106]</th> <td>   23.6330</td> <td>    0.426</td> <td>   55.497</td> <td> 0.000</td> <td>   22.798</td> <td>   24.468</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[107]</th> <td>   31.8083</td> <td>    0.424</td> <td>   75.075</td> <td> 0.000</td> <td>   30.978</td> <td>   32.639</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -1.4356</td> <td>    1.733</td> <td>   -0.828</td> <td> 0.408</td> <td>   -4.832</td> <td>    1.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -1.3541</td> <td>    1.875</td> <td>   -0.722</td> <td> 0.470</td> <td>   -5.028</td> <td>    2.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -0.9835</td> <td>    1.767</td> <td>   -0.557</td> <td> 0.578</td> <td>   -4.447</td> <td>    2.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>    0.1937</td> <td>    1.087</td> <td>    0.178</td> <td> 0.859</td> <td>   -1.937</td> <td>    2.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>    0.5954</td> <td>    0.560</td> <td>    1.063</td> <td> 0.288</td> <td>   -0.503</td> <td>    1.693</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>   -0.9216</td> <td>    1.821</td> <td>   -0.506</td> <td> 0.613</td> <td>   -4.490</td> <td>    2.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>    6.4736</td> <td>    1.261</td> <td>    5.134</td> <td> 0.000</td> <td>    4.002</td> <td>    8.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    3.4386</td> <td>    1.029</td> <td>    3.343</td> <td> 0.001</td> <td>    1.422</td> <td>    5.455</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    2.8745</td> <td>    1.007</td> <td>    2.854</td> <td> 0.004</td> <td>    0.900</td> <td>    4.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    3.7565</td> <td>    1.643</td> <td>    2.286</td> <td> 0.022</td> <td>    0.536</td> <td>    6.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    0.6196</td> <td>    0.716</td> <td>    0.866</td> <td> 0.387</td> <td>   -0.783</td> <td>    2.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    1.3054</td> <td>    0.557</td> <td>    2.343</td> <td> 0.019</td> <td>    0.214</td> <td>    2.397</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    1.9512</td> <td>    0.753</td> <td>    2.592</td> <td> 0.010</td> <td>    0.476</td> <td>    3.427</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    1.9769</td> <td>    1.181</td> <td>    1.675</td> <td> 0.094</td> <td>   -0.337</td> <td>    4.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    2.7474</td> <td>    1.304</td> <td>    2.108</td> <td> 0.035</td> <td>    0.192</td> <td>    5.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    2.4419</td> <td>    1.137</td> <td>    2.148</td> <td> 0.032</td> <td>    0.214</td> <td>    4.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    2.0961</td> <td>    0.695</td> <td>    3.018</td> <td> 0.003</td> <td>    0.735</td> <td>    3.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    0.9545</td> <td>    0.774</td> <td>    1.234</td> <td> 0.217</td> <td>   -0.562</td> <td>    2.471</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    2.0708</td> <td>    0.961</td> <td>    2.155</td> <td> 0.031</td> <td>    0.188</td> <td>    3.954</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    3.4602</td> <td>    1.128</td> <td>    3.069</td> <td> 0.002</td> <td>    1.250</td> <td>    5.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    2.3052</td> <td>    0.885</td> <td>    2.605</td> <td> 0.009</td> <td>    0.571</td> <td>    4.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    2.5295</td> <td>    0.894</td> <td>    2.830</td> <td> 0.005</td> <td>    0.778</td> <td>    4.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    2.4755</td> <td>    0.833</td> <td>    2.971</td> <td> 0.003</td> <td>    0.842</td> <td>    4.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    2.4548</td> <td>    0.749</td> <td>    3.278</td> <td> 0.001</td> <td>    0.987</td> <td>    3.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>    1.1967</td> <td>    0.615</td> <td>    1.947</td> <td> 0.052</td> <td>   -0.008</td> <td>    2.402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    1.7087</td> <td>    0.997</td> <td>    1.715</td> <td> 0.086</td> <td>   -0.244</td> <td>    3.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>    2.9542</td> <td>    0.963</td> <td>    3.069</td> <td> 0.002</td> <td>    1.068</td> <td>    4.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>    2.4737</td> <td>    0.825</td> <td>    2.997</td> <td> 0.003</td> <td>    0.856</td> <td>    4.091</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>6623894.243</td> <th>  Durbin-Watson:     </th>    <td>   1.983</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>30966992513.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td>15.463</td>    <th>  Prob(JB):          </th>    <td>    0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>470.853</td>   <th>  Cond. No.          </th>    <td>    20.2</td>    \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     mobility     & \\textbf{  R-squared:         } &        0.024     \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &        0.024     \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &          nan     \\\\\n",
       "\\textbf{Date:}             & Wed, 20 Aug 2025 & \\textbf{  Prob (F-statistic):} &         nan      \\\\\n",
       "\\textbf{Time:}             &     13:52:48     & \\textbf{  Log-Likelihood:    } &   -1.9537e+07    \\\\\n",
       "\\textbf{No. Observations:} &     3380634      & \\textbf{  AIC:               } &    3.907e+07     \\\\\n",
       "\\textbf{Df Residuals:}     &     3380532      & \\textbf{  BIC:               } &    3.908e+07     \\\\\n",
       "\\textbf{Df Model:}         &         101      & \\textbf{                     } &                  \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[1]}   &      22.6216  &        0.454     &    49.791  &         0.000        &       21.731    &       23.512     \\\\\n",
       "\\textbf{C(strike)[3]}   &      23.7201  &        0.465     &    51.046  &         0.000        &       22.809    &       24.631     \\\\\n",
       "\\textbf{C(strike)[4]}   &      29.1651  &        0.458     &    63.666  &         0.000        &       28.267    &       30.063     \\\\\n",
       "\\textbf{C(strike)[5]}   &      28.2505  &        0.463     &    60.966  &         0.000        &       27.342    &       29.159     \\\\\n",
       "\\textbf{C(strike)[6]}   &      31.4648  &        0.461     &    68.229  &         0.000        &       30.561    &       32.369     \\\\\n",
       "\\textbf{C(strike)[7]}   &      26.7052  &        0.460     &    58.049  &         0.000        &       25.804    &       27.607     \\\\\n",
       "\\textbf{C(strike)[8]}   &      26.3013  &        0.462     &    56.876  &         0.000        &       25.395    &       27.208     \\\\\n",
       "\\textbf{C(strike)[9]}   &      17.7873  &        0.459     &    38.746  &         0.000        &       16.888    &       18.687     \\\\\n",
       "\\textbf{C(strike)[10]}  &      21.8015  &        0.461     &    47.304  &         0.000        &       20.898    &       22.705     \\\\\n",
       "\\textbf{C(strike)[11]}  &      30.5712  &        0.461     &    66.358  &         0.000        &       29.668    &       31.474     \\\\\n",
       "\\textbf{C(strike)[12]}  &      21.2926  &        0.444     &    47.979  &         0.000        &       20.423    &       22.162     \\\\\n",
       "\\textbf{C(strike)[14]}  &      15.3721  &        0.468     &    32.821  &         0.000        &       14.454    &       16.290     \\\\\n",
       "\\textbf{C(strike)[15]}  &      26.9420  &        0.460     &    58.541  &         0.000        &       26.040    &       27.844     \\\\\n",
       "\\textbf{C(strike)[17]}  &      22.3700  &        0.462     &    48.427  &         0.000        &       21.465    &       23.275     \\\\\n",
       "\\textbf{C(strike)[19]}  &      21.2447  &        0.462     &    45.966  &         0.000        &       20.339    &       22.151     \\\\\n",
       "\\textbf{C(strike)[20]}  &      22.1546  &        0.458     &    48.374  &         0.000        &       21.257    &       23.052     \\\\\n",
       "\\textbf{C(strike)[24]}  &      39.7041  &        0.465     &    85.294  &         0.000        &       38.792    &       40.616     \\\\\n",
       "\\textbf{C(strike)[26]}  &      23.2795  &        0.435     &    53.539  &         0.000        &       22.427    &       24.132     \\\\\n",
       "\\textbf{C(strike)[27]}  &      21.5946  &        0.466     &    46.308  &         0.000        &       20.681    &       22.509     \\\\\n",
       "\\textbf{C(strike)[28]}  &      22.7090  &        0.465     &    48.825  &         0.000        &       21.797    &       23.621     \\\\\n",
       "\\textbf{C(strike)[31]}  &      17.3884  &        0.462     &    37.664  &         0.000        &       16.484    &       18.293     \\\\\n",
       "\\textbf{C(strike)[32]}  &      31.0847  &        0.460     &    67.563  &         0.000        &       30.183    &       31.986     \\\\\n",
       "\\textbf{C(strike)[33]}  &      23.1976  &        0.461     &    50.288  &         0.000        &       22.294    &       24.102     \\\\\n",
       "\\textbf{C(strike)[34]}  &      16.0935  &        0.457     &    35.191  &         0.000        &       15.197    &       16.990     \\\\\n",
       "\\textbf{C(strike)[35]}  &      16.0875  &        0.462     &    34.854  &         0.000        &       15.183    &       16.992     \\\\\n",
       "\\textbf{C(strike)[36]}  &      23.1653  &        0.462     &    50.138  &         0.000        &       22.260    &       24.071     \\\\\n",
       "\\textbf{C(strike)[37]}  &      18.1846  &        0.461     &    39.449  &         0.000        &       17.281    &       19.088     \\\\\n",
       "\\textbf{C(strike)[38]}  &      15.2232  &        0.458     &    33.257  &         0.000        &       14.326    &       16.120     \\\\\n",
       "\\textbf{C(strike)[39]}  &      21.4850  &        0.462     &    46.532  &         0.000        &       20.580    &       22.390     \\\\\n",
       "\\textbf{C(strike)[40]}  &      18.9805  &        0.462     &    41.040  &         0.000        &       18.074    &       19.887     \\\\\n",
       "\\textbf{C(strike)[41]}  &      18.9145  &        0.461     &    40.986  &         0.000        &       18.010    &       19.819     \\\\\n",
       "\\textbf{C(strike)[42]}  &      17.3487  &        0.462     &    37.553  &         0.000        &       16.443    &       18.254     \\\\\n",
       "\\textbf{C(strike)[43]}  &      24.7589  &        0.462     &    53.540  &         0.000        &       23.853    &       25.665     \\\\\n",
       "\\textbf{C(strike)[45]}  &      59.9838  &        0.466     &   128.703  &         0.000        &       59.070    &       60.897     \\\\\n",
       "\\textbf{C(strike)[47]}  &      34.2409  &        0.457     &    74.959  &         0.000        &       33.346    &       35.136     \\\\\n",
       "\\textbf{C(strike)[48]}  &      18.8975  &        0.459     &    41.170  &         0.000        &       17.998    &       19.797     \\\\\n",
       "\\textbf{C(strike)[49]}  &      30.6192  &        0.460     &    66.514  &         0.000        &       29.717    &       31.521     \\\\\n",
       "\\textbf{C(strike)[50]}  &      32.7739  &        0.461     &    71.036  &         0.000        &       31.870    &       33.678     \\\\\n",
       "\\textbf{C(strike)[51]}  &      42.6047  &        0.463     &    91.957  &         0.000        &       41.697    &       43.513     \\\\\n",
       "\\textbf{C(strike)[54]}  &      27.3064  &        0.462     &    59.042  &         0.000        &       26.400    &       28.213     \\\\\n",
       "\\textbf{C(strike)[56]}  &      24.2100  &        0.461     &    52.527  &         0.000        &       23.307    &       25.113     \\\\\n",
       "\\textbf{C(strike)[57]}  &      31.3027  &        0.461     &    67.925  &         0.000        &       30.399    &       32.206     \\\\\n",
       "\\textbf{C(strike)[58]}  &      26.1722  &        0.458     &    57.084  &         0.000        &       25.274    &       27.071     \\\\\n",
       "\\textbf{C(strike)[59]}  &      18.2384  &        0.463     &    39.423  &         0.000        &       17.332    &       19.145     \\\\\n",
       "\\textbf{C(strike)[60]}  &      28.5808  &        0.452     &    63.203  &         0.000        &       27.694    &       29.467     \\\\\n",
       "\\textbf{C(strike)[61]}  &      22.3607  &        0.450     &    49.683  &         0.000        &       21.479    &       23.243     \\\\\n",
       "\\textbf{C(strike)[62]}  &      27.2163  &        0.462     &    58.897  &         0.000        &       26.311    &       28.122     \\\\\n",
       "\\textbf{C(strike)[64]}  &      32.8167  &        0.461     &    71.161  &         0.000        &       31.913    &       33.721     \\\\\n",
       "\\textbf{C(strike)[65]}  &      40.9012  &        0.457     &    89.436  &         0.000        &       40.005    &       41.798     \\\\\n",
       "\\textbf{C(strike)[67]}  &      29.6181  &        0.448     &    66.148  &         0.000        &       28.741    &       30.496     \\\\\n",
       "\\textbf{C(strike)[68]}  &      27.8218  &        0.445     &    62.460  &         0.000        &       26.949    &       28.695     \\\\\n",
       "\\textbf{C(strike)[69]}  &      33.2619  &        0.458     &    72.570  &         0.000        &       32.364    &       34.160     \\\\\n",
       "\\textbf{C(strike)[70]}  &      23.8595  &        0.466     &    51.147  &         0.000        &       22.945    &       24.774     \\\\\n",
       "\\textbf{C(strike)[71]}  &      32.2812  &        0.458     &    70.498  &         0.000        &       31.384    &       33.179     \\\\\n",
       "\\textbf{C(strike)[72]}  &      34.3652  &        0.461     &    74.517  &         0.000        &       33.461    &       35.269     \\\\\n",
       "\\textbf{C(strike)[75]}  &      39.9147  &        0.462     &    86.360  &         0.000        &       39.009    &       40.821     \\\\\n",
       "\\textbf{C(strike)[76]}  &      62.2330  &        0.463     &   134.494  &         0.000        &       61.326    &       63.140     \\\\\n",
       "\\textbf{C(strike)[77]}  &      31.1554  &        0.462     &    67.383  &         0.000        &       30.249    &       32.062     \\\\\n",
       "\\textbf{C(strike)[78]}  &      27.2946  &        0.464     &    58.848  &         0.000        &       26.386    &       28.204     \\\\\n",
       "\\textbf{C(strike)[81]}  &      39.2854  &        0.456     &    86.132  &         0.000        &       38.391    &       40.179     \\\\\n",
       "\\textbf{C(strike)[82]}  &      27.1925  &        0.459     &    59.215  &         0.000        &       26.292    &       28.093     \\\\\n",
       "\\textbf{C(strike)[83]}  &      27.6238  &        0.458     &    60.319  &         0.000        &       26.726    &       28.521     \\\\\n",
       "\\textbf{C(strike)[85]}  &      26.2821  &        0.457     &    57.529  &         0.000        &       25.387    &       27.178     \\\\\n",
       "\\textbf{C(strike)[87]}  &      20.0700  &        0.461     &    43.498  &         0.000        &       19.166    &       20.974     \\\\\n",
       "\\textbf{C(strike)[90]}  &      14.8231  &        0.459     &    32.264  &         0.000        &       13.923    &       15.724     \\\\\n",
       "\\textbf{C(strike)[92]}  &      15.5966  &        0.461     &    33.855  &         0.000        &       14.694    &       16.499     \\\\\n",
       "\\textbf{C(strike)[95]}  &      19.2778  &        0.468     &    41.200  &         0.000        &       18.361    &       20.195     \\\\\n",
       "\\textbf{C(strike)[96]}  &      40.2916  &        0.462     &    87.303  &         0.000        &       39.387    &       41.196     \\\\\n",
       "\\textbf{C(strike)[97]}  &      14.0519  &        0.462     &    30.387  &         0.000        &       13.146    &       14.958     \\\\\n",
       "\\textbf{C(strike)[100]} &      29.3106  &        0.460     &    63.781  &         0.000        &       28.410    &       30.211     \\\\\n",
       "\\textbf{C(strike)[101]} &      44.1617  &        0.460     &    96.099  &         0.000        &       43.261    &       45.062     \\\\\n",
       "\\textbf{C(strike)[102]} &      34.2295  &        0.461     &    74.291  &         0.000        &       33.326    &       35.133     \\\\\n",
       "\\textbf{C(strike)[106]} &      23.6330  &        0.426     &    55.497  &         0.000        &       22.798    &       24.468     \\\\\n",
       "\\textbf{C(strike)[107]} &      31.8083  &        0.424     &    75.075  &         0.000        &       30.978    &       32.639     \\\\\n",
       "\\textbf{X\\_1}           &      -1.4356  &        1.733     &    -0.828  &         0.408        &       -4.832    &        1.961     \\\\\n",
       "\\textbf{X\\_2}           &      -1.3541  &        1.875     &    -0.722  &         0.470        &       -5.028    &        2.320     \\\\\n",
       "\\textbf{X\\_3}           &      -0.9835  &        1.767     &    -0.557  &         0.578        &       -4.447    &        2.480     \\\\\n",
       "\\textbf{X\\_4}           &       0.1937  &        1.087     &     0.178  &         0.859        &       -1.937    &        2.325     \\\\\n",
       "\\textbf{X\\_5}           &       0.5954  &        0.560     &     1.063  &         0.288        &       -0.503    &        1.693     \\\\\n",
       "\\textbf{X\\_7}           &      -0.9216  &        1.821     &    -0.506  &         0.613        &       -4.490    &        2.647     \\\\\n",
       "\\textbf{X\\_8}           &       6.4736  &        1.261     &     5.134  &         0.000        &        4.002    &        8.945     \\\\\n",
       "\\textbf{X\\_9}           &       3.4386  &        1.029     &     3.343  &         0.001        &        1.422    &        5.455     \\\\\n",
       "\\textbf{X\\_10}          &       2.8745  &        1.007     &     2.854  &         0.004        &        0.900    &        4.849     \\\\\n",
       "\\textbf{X\\_11}          &       3.7565  &        1.643     &     2.286  &         0.022        &        0.536    &        6.977     \\\\\n",
       "\\textbf{X\\_12}          &       0.6196  &        0.716     &     0.866  &         0.387        &       -0.783    &        2.023     \\\\\n",
       "\\textbf{X\\_13}          &       1.3054  &        0.557     &     2.343  &         0.019        &        0.214    &        2.397     \\\\\n",
       "\\textbf{X\\_14}          &       1.9512  &        0.753     &     2.592  &         0.010        &        0.476    &        3.427     \\\\\n",
       "\\textbf{X\\_15}          &       1.9769  &        1.181     &     1.675  &         0.094        &       -0.337    &        4.291     \\\\\n",
       "\\textbf{X\\_16}          &       2.7474  &        1.304     &     2.108  &         0.035        &        0.192    &        5.302     \\\\\n",
       "\\textbf{X\\_17}          &       2.4419  &        1.137     &     2.148  &         0.032        &        0.214    &        4.670     \\\\\n",
       "\\textbf{X\\_18}          &       2.0961  &        0.695     &     3.018  &         0.003        &        0.735    &        3.458     \\\\\n",
       "\\textbf{X\\_19}          &       0.9545  &        0.774     &     1.234  &         0.217        &       -0.562    &        2.471     \\\\\n",
       "\\textbf{X\\_20}          &       2.0708  &        0.961     &     2.155  &         0.031        &        0.188    &        3.954     \\\\\n",
       "\\textbf{X\\_21}          &       3.4602  &        1.128     &     3.069  &         0.002        &        1.250    &        5.670     \\\\\n",
       "\\textbf{X\\_22}          &       2.3052  &        0.885     &     2.605  &         0.009        &        0.571    &        4.040     \\\\\n",
       "\\textbf{X\\_23}          &       2.5295  &        0.894     &     2.830  &         0.005        &        0.778    &        4.281     \\\\\n",
       "\\textbf{X\\_24}          &       2.4755  &        0.833     &     2.971  &         0.003        &        0.842    &        4.109     \\\\\n",
       "\\textbf{X\\_25}          &       2.4548  &        0.749     &     3.278  &         0.001        &        0.987    &        3.923     \\\\\n",
       "\\textbf{X\\_26}          &       1.1967  &        0.615     &     1.947  &         0.052        &       -0.008    &        2.402     \\\\\n",
       "\\textbf{X\\_27}          &       1.7087  &        0.997     &     1.715  &         0.086        &       -0.244    &        3.662     \\\\\n",
       "\\textbf{X\\_28}          &       2.9542  &        0.963     &     3.069  &         0.002        &        1.068    &        4.841     \\\\\n",
       "\\textbf{X\\_29}          &       2.4737  &        0.825     &     2.997  &         0.003        &        0.856    &        4.091     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 6623894.243 & \\textbf{  Durbin-Watson:     } &        1.983     \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 30966992513.040  \\\\\n",
       "\\textbf{Skew:}          &    15.463   & \\textbf{  Prob(JB):          } &         0.00     \\\\\n",
       "\\textbf{Kurtosis:}      &   470.853   & \\textbf{  Cond. No.          } &         20.2     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.024\n",
       "Model:                            OLS   Adj. R-squared:                  0.024\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 20 Aug 2025   Prob (F-statistic):                nan\n",
       "Time:                        13:52:48   Log-Likelihood:            -1.9537e+07\n",
       "No. Observations:             3380634   AIC:                         3.907e+07\n",
       "Df Residuals:                 3380532   BIC:                         3.908e+07\n",
       "Df Model:                         101                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[1]      22.6216      0.454     49.791      0.000      21.731      23.512\n",
       "C(strike)[3]      23.7201      0.465     51.046      0.000      22.809      24.631\n",
       "C(strike)[4]      29.1651      0.458     63.666      0.000      28.267      30.063\n",
       "C(strike)[5]      28.2505      0.463     60.966      0.000      27.342      29.159\n",
       "C(strike)[6]      31.4648      0.461     68.229      0.000      30.561      32.369\n",
       "C(strike)[7]      26.7052      0.460     58.049      0.000      25.804      27.607\n",
       "C(strike)[8]      26.3013      0.462     56.876      0.000      25.395      27.208\n",
       "C(strike)[9]      17.7873      0.459     38.746      0.000      16.888      18.687\n",
       "C(strike)[10]     21.8015      0.461     47.304      0.000      20.898      22.705\n",
       "C(strike)[11]     30.5712      0.461     66.358      0.000      29.668      31.474\n",
       "C(strike)[12]     21.2926      0.444     47.979      0.000      20.423      22.162\n",
       "C(strike)[14]     15.3721      0.468     32.821      0.000      14.454      16.290\n",
       "C(strike)[15]     26.9420      0.460     58.541      0.000      26.040      27.844\n",
       "C(strike)[17]     22.3700      0.462     48.427      0.000      21.465      23.275\n",
       "C(strike)[19]     21.2447      0.462     45.966      0.000      20.339      22.151\n",
       "C(strike)[20]     22.1546      0.458     48.374      0.000      21.257      23.052\n",
       "C(strike)[24]     39.7041      0.465     85.294      0.000      38.792      40.616\n",
       "C(strike)[26]     23.2795      0.435     53.539      0.000      22.427      24.132\n",
       "C(strike)[27]     21.5946      0.466     46.308      0.000      20.681      22.509\n",
       "C(strike)[28]     22.7090      0.465     48.825      0.000      21.797      23.621\n",
       "C(strike)[31]     17.3884      0.462     37.664      0.000      16.484      18.293\n",
       "C(strike)[32]     31.0847      0.460     67.563      0.000      30.183      31.986\n",
       "C(strike)[33]     23.1976      0.461     50.288      0.000      22.294      24.102\n",
       "C(strike)[34]     16.0935      0.457     35.191      0.000      15.197      16.990\n",
       "C(strike)[35]     16.0875      0.462     34.854      0.000      15.183      16.992\n",
       "C(strike)[36]     23.1653      0.462     50.138      0.000      22.260      24.071\n",
       "C(strike)[37]     18.1846      0.461     39.449      0.000      17.281      19.088\n",
       "C(strike)[38]     15.2232      0.458     33.257      0.000      14.326      16.120\n",
       "C(strike)[39]     21.4850      0.462     46.532      0.000      20.580      22.390\n",
       "C(strike)[40]     18.9805      0.462     41.040      0.000      18.074      19.887\n",
       "C(strike)[41]     18.9145      0.461     40.986      0.000      18.010      19.819\n",
       "C(strike)[42]     17.3487      0.462     37.553      0.000      16.443      18.254\n",
       "C(strike)[43]     24.7589      0.462     53.540      0.000      23.853      25.665\n",
       "C(strike)[45]     59.9838      0.466    128.703      0.000      59.070      60.897\n",
       "C(strike)[47]     34.2409      0.457     74.959      0.000      33.346      35.136\n",
       "C(strike)[48]     18.8975      0.459     41.170      0.000      17.998      19.797\n",
       "C(strike)[49]     30.6192      0.460     66.514      0.000      29.717      31.521\n",
       "C(strike)[50]     32.7739      0.461     71.036      0.000      31.870      33.678\n",
       "C(strike)[51]     42.6047      0.463     91.957      0.000      41.697      43.513\n",
       "C(strike)[54]     27.3064      0.462     59.042      0.000      26.400      28.213\n",
       "C(strike)[56]     24.2100      0.461     52.527      0.000      23.307      25.113\n",
       "C(strike)[57]     31.3027      0.461     67.925      0.000      30.399      32.206\n",
       "C(strike)[58]     26.1722      0.458     57.084      0.000      25.274      27.071\n",
       "C(strike)[59]     18.2384      0.463     39.423      0.000      17.332      19.145\n",
       "C(strike)[60]     28.5808      0.452     63.203      0.000      27.694      29.467\n",
       "C(strike)[61]     22.3607      0.450     49.683      0.000      21.479      23.243\n",
       "C(strike)[62]     27.2163      0.462     58.897      0.000      26.311      28.122\n",
       "C(strike)[64]     32.8167      0.461     71.161      0.000      31.913      33.721\n",
       "C(strike)[65]     40.9012      0.457     89.436      0.000      40.005      41.798\n",
       "C(strike)[67]     29.6181      0.448     66.148      0.000      28.741      30.496\n",
       "C(strike)[68]     27.8218      0.445     62.460      0.000      26.949      28.695\n",
       "C(strike)[69]     33.2619      0.458     72.570      0.000      32.364      34.160\n",
       "C(strike)[70]     23.8595      0.466     51.147      0.000      22.945      24.774\n",
       "C(strike)[71]     32.2812      0.458     70.498      0.000      31.384      33.179\n",
       "C(strike)[72]     34.3652      0.461     74.517      0.000      33.461      35.269\n",
       "C(strike)[75]     39.9147      0.462     86.360      0.000      39.009      40.821\n",
       "C(strike)[76]     62.2330      0.463    134.494      0.000      61.326      63.140\n",
       "C(strike)[77]     31.1554      0.462     67.383      0.000      30.249      32.062\n",
       "C(strike)[78]     27.2946      0.464     58.848      0.000      26.386      28.204\n",
       "C(strike)[81]     39.2854      0.456     86.132      0.000      38.391      40.179\n",
       "C(strike)[82]     27.1925      0.459     59.215      0.000      26.292      28.093\n",
       "C(strike)[83]     27.6238      0.458     60.319      0.000      26.726      28.521\n",
       "C(strike)[85]     26.2821      0.457     57.529      0.000      25.387      27.178\n",
       "C(strike)[87]     20.0700      0.461     43.498      0.000      19.166      20.974\n",
       "C(strike)[90]     14.8231      0.459     32.264      0.000      13.923      15.724\n",
       "C(strike)[92]     15.5966      0.461     33.855      0.000      14.694      16.499\n",
       "C(strike)[95]     19.2778      0.468     41.200      0.000      18.361      20.195\n",
       "C(strike)[96]     40.2916      0.462     87.303      0.000      39.387      41.196\n",
       "C(strike)[97]     14.0519      0.462     30.387      0.000      13.146      14.958\n",
       "C(strike)[100]    29.3106      0.460     63.781      0.000      28.410      30.211\n",
       "C(strike)[101]    44.1617      0.460     96.099      0.000      43.261      45.062\n",
       "C(strike)[102]    34.2295      0.461     74.291      0.000      33.326      35.133\n",
       "C(strike)[106]    23.6330      0.426     55.497      0.000      22.798      24.468\n",
       "C(strike)[107]    31.8083      0.424     75.075      0.000      30.978      32.639\n",
       "X_1               -1.4356      1.733     -0.828      0.408      -4.832       1.961\n",
       "X_2               -1.3541      1.875     -0.722      0.470      -5.028       2.320\n",
       "X_3               -0.9835      1.767     -0.557      0.578      -4.447       2.480\n",
       "X_4                0.1937      1.087      0.178      0.859      -1.937       2.325\n",
       "X_5                0.5954      0.560      1.063      0.288      -0.503       1.693\n",
       "X_7               -0.9216      1.821     -0.506      0.613      -4.490       2.647\n",
       "X_8                6.4736      1.261      5.134      0.000       4.002       8.945\n",
       "X_9                3.4386      1.029      3.343      0.001       1.422       5.455\n",
       "X_10               2.8745      1.007      2.854      0.004       0.900       4.849\n",
       "X_11               3.7565      1.643      2.286      0.022       0.536       6.977\n",
       "X_12               0.6196      0.716      0.866      0.387      -0.783       2.023\n",
       "X_13               1.3054      0.557      2.343      0.019       0.214       2.397\n",
       "X_14               1.9512      0.753      2.592      0.010       0.476       3.427\n",
       "X_15               1.9769      1.181      1.675      0.094      -0.337       4.291\n",
       "X_16               2.7474      1.304      2.108      0.035       0.192       5.302\n",
       "X_17               2.4419      1.137      2.148      0.032       0.214       4.670\n",
       "X_18               2.0961      0.695      3.018      0.003       0.735       3.458\n",
       "X_19               0.9545      0.774      1.234      0.217      -0.562       2.471\n",
       "X_20               2.0708      0.961      2.155      0.031       0.188       3.954\n",
       "X_21               3.4602      1.128      3.069      0.002       1.250       5.670\n",
       "X_22               2.3052      0.885      2.605      0.009       0.571       4.040\n",
       "X_23               2.5295      0.894      2.830      0.005       0.778       4.281\n",
       "X_24               2.4755      0.833      2.971      0.003       0.842       4.109\n",
       "X_25               2.4548      0.749      3.278      0.001       0.987       3.923\n",
       "X_26               1.1967      0.615      1.947      0.052      -0.008       2.402\n",
       "X_27               1.7087      0.997      1.715      0.086      -0.244       3.662\n",
       "X_28               2.9542      0.963      3.069      0.002       1.068       4.841\n",
       "X_29               2.4737      0.825      2.997      0.003       0.856       4.091\n",
       "==============================================================================\n",
       "Omnibus:                  6623894.243   Durbin-Watson:                   1.983\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):      30966992513.040\n",
       "Skew:                          15.463   Prob(JB):                         0.00\n",
       "Kurtosis:                     470.853   Cond. No.                         20.2\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' + C(strike) - 1', \n",
    "             data=df).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reindex the results to go from day = -7 to day = 21\n",
    "event_res = res[res.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res = event_res.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res['day'] = event_res['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure 2a: average daily distance travelled by proximal individuals around the 74 strikes\n",
    "# 95% confidence intervals are show in light blue\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.gca()\n",
    "\n",
    "ax.plot(event_res['day'], event_res['params'], color='C0', zorder=3)\n",
    "ax.fill_between(event_res['day'], event_res['ci_l'], event_res['ci_h'], \n",
    "                facecolor='C0', alpha=0.2, zorder=2)\n",
    "ax.axhline(y=0, color='k', alpha=0.7, linestyle='--', zorder=1)\n",
    "ax.axvline(x=0, color='k', alpha=0.3, linestyle='--', zorder=1)\n",
    "\n",
    "ax.set_xlim([-7, 21])\n",
    "ax.set_ylim([-7, 11])\n",
    "ax.set_ylabel('Distance (km)')\n",
    "ax.set_xlabel('Days since strike')\n",
    "\n",
    "fig = ax.get_figure()\n",
    "fig.set_size_inches(10, 6)\n",
    "fig.savefig('figures/figure2a.png', bbox_inches='tight', format='png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get mobility increase on strike days compared to pre-strike mean\n",
    "\n",
    "mobility_onstrike = df[df['day'] == shift]['mobility'].mean()\n",
    "mobility_prestrike = df[df['day'] < shift]['mobility'].mean()\n",
    "mobility_increase = (mobility_onstrike - mobility_prestrike) / mobility_prestrike\n",
    "\n",
    "print('Mobility increases %.1f%% on strike days compared to the pre-strike mean' %(mobility_increase*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 2b: Distance of individuals to the strike region around strikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load distance data\n",
    "distance = pd.read_csv('data_mobility/distance_daily.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strike</th>\n",
       "      <th>day</th>\n",
       "      <th>id</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>15700</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>362258</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>546570</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>578001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>581213</td>\n",
       "      <td>52.588072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strike  day      id   distance\n",
       "0       1   -7   15700   0.000000\n",
       "1       1   -7  362258   0.000000\n",
       "2       1   -7  546570   0.000000\n",
       "3       1   -7  578001   0.000000\n",
       "4       1   -7  581213  52.588072"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the distance dataframe records the distance of proximal individuals to the strike region\n",
    "# each day for 7 days before the strike, on the day of the strike, and 56 days after\n",
    "# the strike column records the strike ID, the day column records the number of days to the strike,\n",
    "# the id column records a unique anonymized ID for each individual, and\n",
    "# the distance column records the distance from the strike region in miles\n",
    "distance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prep distance data for event study regression\n",
    "\n",
    "# shift the day index to only include positive values for the regression\n",
    "shift = 8\n",
    "distance['day'] = distance['day'] + shift\n",
    "\n",
    "# add lead/lag indicator variables corresponding to the day index\n",
    "xs = pd.get_dummies(distance['day'], prefix='X')\n",
    "df = distance.merge(xs, left_index=True, right_index=True)\n",
    "\n",
    "# Ensure X columns are numeric, not boolean\n",
    "for col in df.columns:\n",
    "    if col.startswith(\"X_\"):\n",
    "        df[col] = df[col].astype(int)\n",
    "\n",
    "# convert mobility in miles to kilometers\n",
    "km_scalar = 1.60934    # scalar to convert miles to kilometers\n",
    "df['distance'] = df['distance'] * km_scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create string that includes all the lead/lag indicator variables\n",
    "# this string will be used in the statsmodel regression formula\n",
    "# drop the -2 day variable to avoid multicollinearity\n",
    "\n",
    "var = []\n",
    "for i in range(-7, 56+1):\n",
    "    if i == -2:\n",
    "        continue\n",
    "    var.append('X_' + str(i+shift))\n",
    "var_form = ' + '.join(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>distance</td>     <th>  R-squared:         </th>  <td>   0.029</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.029</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 20 Aug 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:55:34</td>     <th>  Log-Likelihood:    </th> <td>-1.6456e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>2949077</td>     <th>  AIC:               </th>  <td>3.291e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>2948940</td>     <th>  BIC:               </th>  <td>3.291e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   136</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[1]</th>   <td>   16.0187</td> <td>    1.178</td> <td>   13.601</td> <td> 0.000</td> <td>   13.710</td> <td>   18.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[3]</th>   <td>   13.6911</td> <td>    1.207</td> <td>   11.343</td> <td> 0.000</td> <td>   11.325</td> <td>   16.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[4]</th>   <td>    9.0617</td> <td>    1.182</td> <td>    7.665</td> <td> 0.000</td> <td>    6.745</td> <td>   11.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[5]</th>   <td>   25.3413</td> <td>    1.225</td> <td>   20.683</td> <td> 0.000</td> <td>   22.940</td> <td>   27.743</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[6]</th>   <td>   25.2748</td> <td>    1.233</td> <td>   20.491</td> <td> 0.000</td> <td>   22.857</td> <td>   27.692</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[7]</th>   <td>   42.0044</td> <td>    1.146</td> <td>   36.643</td> <td> 0.000</td> <td>   39.758</td> <td>   44.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[8]</th>   <td>    4.9712</td> <td>    1.220</td> <td>    4.076</td> <td> 0.000</td> <td>    2.581</td> <td>    7.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[9]</th>   <td>    9.7682</td> <td>    1.131</td> <td>    8.635</td> <td> 0.000</td> <td>    7.551</td> <td>   11.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[10]</th>  <td>   16.8084</td> <td>    1.094</td> <td>   15.367</td> <td> 0.000</td> <td>   14.665</td> <td>   18.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[11]</th>  <td>   12.9812</td> <td>    1.056</td> <td>   12.294</td> <td> 0.000</td> <td>   10.912</td> <td>   15.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[12]</th>  <td>   12.5542</td> <td>    1.380</td> <td>    9.099</td> <td> 0.000</td> <td>    9.850</td> <td>   15.258</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[14]</th>  <td>   20.8733</td> <td>    1.177</td> <td>   17.736</td> <td> 0.000</td> <td>   18.567</td> <td>   23.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[15]</th>  <td>   12.6472</td> <td>    0.657</td> <td>   19.258</td> <td> 0.000</td> <td>   11.360</td> <td>   13.934</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[17]</th>  <td>   21.7201</td> <td>    1.126</td> <td>   19.284</td> <td> 0.000</td> <td>   19.512</td> <td>   23.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[19]</th>  <td>   23.7198</td> <td>    1.001</td> <td>   23.703</td> <td> 0.000</td> <td>   21.758</td> <td>   25.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[20]</th>  <td>   26.8402</td> <td>    1.066</td> <td>   25.187</td> <td> 0.000</td> <td>   24.752</td> <td>   28.929</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[24]</th>  <td>   60.8126</td> <td>    1.173</td> <td>   51.824</td> <td> 0.000</td> <td>   58.513</td> <td>   63.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[26]</th>  <td>   29.4882</td> <td>    1.156</td> <td>   25.505</td> <td> 0.000</td> <td>   27.222</td> <td>   31.754</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[27]</th>  <td>   31.0061</td> <td>    1.138</td> <td>   27.247</td> <td> 0.000</td> <td>   28.776</td> <td>   33.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[28]</th>  <td>   21.5704</td> <td>    1.062</td> <td>   20.305</td> <td> 0.000</td> <td>   19.488</td> <td>   23.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[31]</th>  <td>   11.9036</td> <td>    1.280</td> <td>    9.302</td> <td> 0.000</td> <td>    9.395</td> <td>   14.412</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[32]</th>  <td>   20.0888</td> <td>    1.227</td> <td>   16.367</td> <td> 0.000</td> <td>   17.683</td> <td>   22.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[33]</th>  <td>   14.5246</td> <td>    1.206</td> <td>   12.041</td> <td> 0.000</td> <td>   12.160</td> <td>   16.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[34]</th>  <td>   12.4340</td> <td>    1.210</td> <td>   10.275</td> <td> 0.000</td> <td>   10.062</td> <td>   14.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[35]</th>  <td>   11.3830</td> <td>    1.187</td> <td>    9.591</td> <td> 0.000</td> <td>    9.057</td> <td>   13.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[36]</th>  <td>    9.8922</td> <td>    1.207</td> <td>    8.197</td> <td> 0.000</td> <td>    7.527</td> <td>   12.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[37]</th>  <td>    7.3523</td> <td>    1.211</td> <td>    6.072</td> <td> 0.000</td> <td>    4.979</td> <td>    9.725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[38]</th>  <td>    8.6382</td> <td>    1.145</td> <td>    7.545</td> <td> 0.000</td> <td>    6.394</td> <td>   10.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[39]</th>  <td>    9.0848</td> <td>    1.195</td> <td>    7.601</td> <td> 0.000</td> <td>    6.742</td> <td>   11.427</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[40]</th>  <td>   29.1468</td> <td>    1.241</td> <td>   23.486</td> <td> 0.000</td> <td>   26.714</td> <td>   31.579</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[41]</th>  <td>   10.3589</td> <td>    1.209</td> <td>    8.567</td> <td> 0.000</td> <td>    7.989</td> <td>   12.729</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[42]</th>  <td>   19.8789</td> <td>    1.220</td> <td>   16.295</td> <td> 0.000</td> <td>   17.488</td> <td>   22.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[43]</th>  <td>   21.5096</td> <td>    1.097</td> <td>   19.615</td> <td> 0.000</td> <td>   19.360</td> <td>   23.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[45]</th>  <td>   96.9802</td> <td>    1.100</td> <td>   88.135</td> <td> 0.000</td> <td>   94.824</td> <td>   99.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[47]</th>  <td>   14.4997</td> <td>    1.204</td> <td>   12.041</td> <td> 0.000</td> <td>   12.140</td> <td>   16.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[48]</th>  <td>   24.5348</td> <td>    1.197</td> <td>   20.492</td> <td> 0.000</td> <td>   22.188</td> <td>   26.881</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[49]</th>  <td>   17.3984</td> <td>    1.208</td> <td>   14.404</td> <td> 0.000</td> <td>   15.031</td> <td>   19.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[50]</th>  <td>   15.0687</td> <td>    1.229</td> <td>   12.257</td> <td> 0.000</td> <td>   12.659</td> <td>   17.478</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[51]</th>  <td>   61.0146</td> <td>    1.174</td> <td>   51.988</td> <td> 0.000</td> <td>   58.714</td> <td>   63.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[54]</th>  <td>   21.4804</td> <td>    1.228</td> <td>   17.489</td> <td> 0.000</td> <td>   19.073</td> <td>   23.888</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[56]</th>  <td>   28.5644</td> <td>    1.130</td> <td>   25.268</td> <td> 0.000</td> <td>   26.349</td> <td>   30.780</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[57]</th>  <td>   16.3145</td> <td>    1.222</td> <td>   13.347</td> <td> 0.000</td> <td>   13.919</td> <td>   18.710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[58]</th>  <td>   25.1248</td> <td>    1.102</td> <td>   22.802</td> <td> 0.000</td> <td>   22.965</td> <td>   27.284</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[59]</th>  <td>   17.7119</td> <td>    1.239</td> <td>   14.294</td> <td> 0.000</td> <td>   15.283</td> <td>   20.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[60]</th>  <td>   23.2158</td> <td>    1.069</td> <td>   21.708</td> <td> 0.000</td> <td>   21.120</td> <td>   25.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[61]</th>  <td>   25.0987</td> <td>    1.174</td> <td>   21.382</td> <td> 0.000</td> <td>   22.798</td> <td>   27.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[62]</th>  <td>   44.3749</td> <td>    1.188</td> <td>   37.367</td> <td> 0.000</td> <td>   42.047</td> <td>   46.702</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[64]</th>  <td>   21.9249</td> <td>    1.195</td> <td>   18.354</td> <td> 0.000</td> <td>   19.584</td> <td>   24.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[65]</th>  <td>   60.4603</td> <td>    1.166</td> <td>   51.863</td> <td> 0.000</td> <td>   58.175</td> <td>   62.745</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[67]</th>  <td>   19.7693</td> <td>    1.068</td> <td>   18.509</td> <td> 0.000</td> <td>   17.676</td> <td>   21.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[68]</th>  <td>   23.4975</td> <td>    1.102</td> <td>   21.322</td> <td> 0.000</td> <td>   21.338</td> <td>   25.657</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[69]</th>  <td>   21.3928</td> <td>    1.071</td> <td>   19.971</td> <td> 0.000</td> <td>   19.293</td> <td>   23.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[70]</th>  <td>   39.6671</td> <td>    1.180</td> <td>   33.608</td> <td> 0.000</td> <td>   37.354</td> <td>   41.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[71]</th>  <td>   70.0063</td> <td>    1.150</td> <td>   60.868</td> <td> 0.000</td> <td>   67.752</td> <td>   72.260</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[72]</th>  <td>   15.5273</td> <td>    1.228</td> <td>   12.647</td> <td> 0.000</td> <td>   13.121</td> <td>   17.934</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[75]</th>  <td>    5.8710</td> <td>    1.196</td> <td>    4.907</td> <td> 0.000</td> <td>    3.526</td> <td>    8.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[76]</th>  <td>   33.4700</td> <td>    1.155</td> <td>   28.971</td> <td> 0.000</td> <td>   31.206</td> <td>   35.734</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[77]</th>  <td>   26.4433</td> <td>    1.225</td> <td>   21.579</td> <td> 0.000</td> <td>   24.042</td> <td>   28.845</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[78]</th>  <td>   22.3530</td> <td>    1.186</td> <td>   18.854</td> <td> 0.000</td> <td>   20.029</td> <td>   24.677</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[81]</th>  <td>   34.7242</td> <td>    1.168</td> <td>   29.740</td> <td> 0.000</td> <td>   32.436</td> <td>   37.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[82]</th>  <td>   38.1832</td> <td>    1.071</td> <td>   35.655</td> <td> 0.000</td> <td>   36.084</td> <td>   40.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[83]</th>  <td>   27.2388</td> <td>    1.042</td> <td>   26.135</td> <td> 0.000</td> <td>   25.196</td> <td>   29.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[85]</th>  <td>   36.4337</td> <td>    1.081</td> <td>   33.694</td> <td> 0.000</td> <td>   34.314</td> <td>   38.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[87]</th>  <td>    9.8531</td> <td>    0.925</td> <td>   10.657</td> <td> 0.000</td> <td>    8.041</td> <td>   11.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[90]</th>  <td>    5.7953</td> <td>    1.296</td> <td>    4.473</td> <td> 0.000</td> <td>    3.256</td> <td>    8.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[92]</th>  <td>   20.7836</td> <td>    1.140</td> <td>   18.224</td> <td> 0.000</td> <td>   18.548</td> <td>   23.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[95]</th>  <td>   62.0763</td> <td>    0.824</td> <td>   75.292</td> <td> 0.000</td> <td>   60.460</td> <td>   63.692</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[96]</th>  <td>    4.7274</td> <td>    1.217</td> <td>    3.884</td> <td> 0.000</td> <td>    2.342</td> <td>    7.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[97]</th>  <td>   29.0708</td> <td>    1.255</td> <td>   23.172</td> <td> 0.000</td> <td>   26.612</td> <td>   31.530</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[100]</th> <td>   93.1699</td> <td>    1.146</td> <td>   81.317</td> <td> 0.000</td> <td>   90.924</td> <td>   95.416</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[101]</th> <td>    5.9158</td> <td>    1.240</td> <td>    4.771</td> <td> 0.000</td> <td>    3.486</td> <td>    8.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[102]</th> <td>   25.7969</td> <td>    1.246</td> <td>   20.699</td> <td> 0.000</td> <td>   23.354</td> <td>   28.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[106]</th> <td>    7.9504</td> <td>    0.541</td> <td>   14.706</td> <td> 0.000</td> <td>    6.891</td> <td>    9.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[107]</th> <td>   51.1471</td> <td>    0.497</td> <td>  102.822</td> <td> 0.000</td> <td>   50.172</td> <td>   52.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -1.1016</td> <td>    0.968</td> <td>   -1.137</td> <td> 0.255</td> <td>   -3.000</td> <td>    0.797</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -0.1545</td> <td>    0.739</td> <td>   -0.209</td> <td> 0.834</td> <td>   -1.603</td> <td>    1.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -0.8605</td> <td>    0.852</td> <td>   -1.010</td> <td> 0.313</td> <td>   -2.531</td> <td>    0.810</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -0.9970</td> <td>    0.811</td> <td>   -1.229</td> <td> 0.219</td> <td>   -2.586</td> <td>    0.592</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>   -0.4134</td> <td>    0.498</td> <td>   -0.830</td> <td> 0.407</td> <td>   -1.390</td> <td>    0.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>    0.4587</td> <td>    0.491</td> <td>    0.933</td> <td> 0.351</td> <td>   -0.505</td> <td>    1.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>   -0.3093</td> <td>    0.549</td> <td>   -0.563</td> <td> 0.573</td> <td>   -1.385</td> <td>    0.767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    2.2095</td> <td>    0.591</td> <td>    3.738</td> <td> 0.000</td> <td>    1.051</td> <td>    3.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    2.8729</td> <td>    0.597</td> <td>    4.813</td> <td> 0.000</td> <td>    1.703</td> <td>    4.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    4.0263</td> <td>    0.688</td> <td>    5.854</td> <td> 0.000</td> <td>    2.678</td> <td>    5.374</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    4.3272</td> <td>    0.744</td> <td>    5.815</td> <td> 0.000</td> <td>    2.869</td> <td>    5.786</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    4.6477</td> <td>    0.805</td> <td>    5.774</td> <td> 0.000</td> <td>    3.070</td> <td>    6.225</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    5.7866</td> <td>    0.872</td> <td>    6.636</td> <td> 0.000</td> <td>    4.078</td> <td>    7.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    5.5172</td> <td>    0.885</td> <td>    6.235</td> <td> 0.000</td> <td>    3.783</td> <td>    7.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    5.9723</td> <td>    0.855</td> <td>    6.987</td> <td> 0.000</td> <td>    4.297</td> <td>    7.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    7.3376</td> <td>    1.186</td> <td>    6.184</td> <td> 0.000</td> <td>    5.012</td> <td>    9.663</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    6.9652</td> <td>    1.087</td> <td>    6.405</td> <td> 0.000</td> <td>    4.834</td> <td>    9.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    6.3606</td> <td>    0.920</td> <td>    6.916</td> <td> 0.000</td> <td>    4.558</td> <td>    8.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    7.0390</td> <td>    1.093</td> <td>    6.437</td> <td> 0.000</td> <td>    4.896</td> <td>    9.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    7.9148</td> <td>    1.053</td> <td>    7.517</td> <td> 0.000</td> <td>    5.851</td> <td>    9.979</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    8.5625</td> <td>    1.051</td> <td>    8.150</td> <td> 0.000</td> <td>    6.503</td> <td>   10.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    8.5001</td> <td>    1.079</td> <td>    7.881</td> <td> 0.000</td> <td>    6.386</td> <td>   10.614</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    9.5089</td> <td>    1.342</td> <td>    7.088</td> <td> 0.000</td> <td>    6.879</td> <td>   12.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    9.2133</td> <td>    1.219</td> <td>    7.561</td> <td> 0.000</td> <td>    6.825</td> <td>   11.602</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>    9.5344</td> <td>    1.356</td> <td>    7.030</td> <td> 0.000</td> <td>    6.876</td> <td>   12.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    9.7210</td> <td>    1.407</td> <td>    6.909</td> <td> 0.000</td> <td>    6.963</td> <td>   12.479</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>   10.2070</td> <td>    1.278</td> <td>    7.988</td> <td> 0.000</td> <td>    7.703</td> <td>   12.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>   11.3109</td> <td>    1.334</td> <td>    8.480</td> <td> 0.000</td> <td>    8.697</td> <td>   13.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_30</th>           <td>   10.6988</td> <td>    1.271</td> <td>    8.417</td> <td> 0.000</td> <td>    8.207</td> <td>   13.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_31</th>           <td>   10.3884</td> <td>    1.304</td> <td>    7.966</td> <td> 0.000</td> <td>    7.833</td> <td>   12.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_32</th>           <td>   10.3415</td> <td>    1.463</td> <td>    7.070</td> <td> 0.000</td> <td>    7.475</td> <td>   13.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_33</th>           <td>   10.3768</td> <td>    1.408</td> <td>    7.370</td> <td> 0.000</td> <td>    7.617</td> <td>   13.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_34</th>           <td>   11.3573</td> <td>    1.612</td> <td>    7.046</td> <td> 0.000</td> <td>    8.198</td> <td>   14.516</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_35</th>           <td>   11.4947</td> <td>    1.520</td> <td>    7.563</td> <td> 0.000</td> <td>    8.516</td> <td>   14.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_36</th>           <td>   11.3062</td> <td>    1.465</td> <td>    7.717</td> <td> 0.000</td> <td>    8.434</td> <td>   14.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_37</th>           <td>   12.0474</td> <td>    1.672</td> <td>    7.205</td> <td> 0.000</td> <td>    8.770</td> <td>   15.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_38</th>           <td>   11.5768</td> <td>    1.716</td> <td>    6.746</td> <td> 0.000</td> <td>    8.213</td> <td>   14.940</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_39</th>           <td>   12.1792</td> <td>    1.810</td> <td>    6.730</td> <td> 0.000</td> <td>    8.632</td> <td>   15.726</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_40</th>           <td>   11.7583</td> <td>    2.011</td> <td>    5.848</td> <td> 0.000</td> <td>    7.817</td> <td>   15.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_41</th>           <td>   11.6675</td> <td>    1.868</td> <td>    6.245</td> <td> 0.000</td> <td>    8.006</td> <td>   15.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_42</th>           <td>   12.5009</td> <td>    1.954</td> <td>    6.399</td> <td> 0.000</td> <td>    8.672</td> <td>   16.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_43</th>           <td>   12.5784</td> <td>    1.830</td> <td>    6.875</td> <td> 0.000</td> <td>    8.993</td> <td>   16.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_44</th>           <td>   12.3761</td> <td>    1.697</td> <td>    7.291</td> <td> 0.000</td> <td>    9.049</td> <td>   15.703</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_45</th>           <td>   13.3908</td> <td>    2.027</td> <td>    6.607</td> <td> 0.000</td> <td>    9.418</td> <td>   17.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_46</th>           <td>   12.7666</td> <td>    1.892</td> <td>    6.746</td> <td> 0.000</td> <td>    9.057</td> <td>   16.476</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_47</th>           <td>   13.3787</td> <td>    2.078</td> <td>    6.437</td> <td> 0.000</td> <td>    9.305</td> <td>   17.452</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_48</th>           <td>   12.8671</td> <td>    2.045</td> <td>    6.293</td> <td> 0.000</td> <td>    8.860</td> <td>   16.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_49</th>           <td>   13.4489</td> <td>    1.974</td> <td>    6.814</td> <td> 0.000</td> <td>    9.580</td> <td>   17.317</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_50</th>           <td>   13.1488</td> <td>    1.873</td> <td>    7.021</td> <td> 0.000</td> <td>    9.478</td> <td>   16.819</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_51</th>           <td>   13.4787</td> <td>    1.858</td> <td>    7.253</td> <td> 0.000</td> <td>    9.836</td> <td>   17.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_52</th>           <td>   13.4598</td> <td>    2.088</td> <td>    6.447</td> <td> 0.000</td> <td>    9.368</td> <td>   17.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_53</th>           <td>   13.1946</td> <td>    2.068</td> <td>    6.381</td> <td> 0.000</td> <td>    9.142</td> <td>   17.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_54</th>           <td>   12.8629</td> <td>    2.227</td> <td>    5.775</td> <td> 0.000</td> <td>    8.498</td> <td>   17.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_55</th>           <td>   13.1867</td> <td>    2.046</td> <td>    6.444</td> <td> 0.000</td> <td>    9.176</td> <td>   17.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_56</th>           <td>   13.4078</td> <td>    2.070</td> <td>    6.476</td> <td> 0.000</td> <td>    9.350</td> <td>   17.466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_57</th>           <td>   12.8493</td> <td>    1.948</td> <td>    6.595</td> <td> 0.000</td> <td>    9.031</td> <td>   16.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_58</th>           <td>   13.0879</td> <td>    2.075</td> <td>    6.306</td> <td> 0.000</td> <td>    9.020</td> <td>   17.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_59</th>           <td>   12.8369</td> <td>    2.115</td> <td>    6.069</td> <td> 0.000</td> <td>    8.691</td> <td>   16.983</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_60</th>           <td>   12.8708</td> <td>    2.372</td> <td>    5.427</td> <td> 0.000</td> <td>    8.222</td> <td>   17.519</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_61</th>           <td>   11.4398</td> <td>    2.344</td> <td>    4.881</td> <td> 0.000</td> <td>    6.846</td> <td>   16.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_62</th>           <td>   12.8966</td> <td>    2.408</td> <td>    5.355</td> <td> 0.000</td> <td>    8.176</td> <td>   17.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_63</th>           <td>   12.7248</td> <td>    2.279</td> <td>    5.583</td> <td> 0.000</td> <td>    8.257</td> <td>   17.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_64</th>           <td>   12.7441</td> <td>    2.298</td> <td>    5.546</td> <td> 0.000</td> <td>    8.241</td> <td>   17.248</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3143791.423</td> <th>  Durbin-Watson:     </th>   <td>   1.975</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>229762476.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 5.475</td>    <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>44.832</td>    <th>  Cond. No.          </th>   <td>    35.9</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     distance     & \\textbf{  R-squared:         } &       0.029    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &       0.029    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &         nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 20 Aug 2025 & \\textbf{  Prob (F-statistic):} &        nan     \\\\\n",
       "\\textbf{Time:}             &     13:55:34     & \\textbf{  Log-Likelihood:    } &  -1.6456e+07   \\\\\n",
       "\\textbf{No. Observations:} &     2949077      & \\textbf{  AIC:               } &   3.291e+07    \\\\\n",
       "\\textbf{Df Residuals:}     &     2948940      & \\textbf{  BIC:               } &   3.291e+07    \\\\\n",
       "\\textbf{Df Model:}         &         136      & \\textbf{                     } &                \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[1]}   &      16.0187  &        1.178     &    13.601  &         0.000        &       13.710    &       18.327     \\\\\n",
       "\\textbf{C(strike)[3]}   &      13.6911  &        1.207     &    11.343  &         0.000        &       11.325    &       16.057     \\\\\n",
       "\\textbf{C(strike)[4]}   &       9.0617  &        1.182     &     7.665  &         0.000        &        6.745    &       11.379     \\\\\n",
       "\\textbf{C(strike)[5]}   &      25.3413  &        1.225     &    20.683  &         0.000        &       22.940    &       27.743     \\\\\n",
       "\\textbf{C(strike)[6]}   &      25.2748  &        1.233     &    20.491  &         0.000        &       22.857    &       27.692     \\\\\n",
       "\\textbf{C(strike)[7]}   &      42.0044  &        1.146     &    36.643  &         0.000        &       39.758    &       44.251     \\\\\n",
       "\\textbf{C(strike)[8]}   &       4.9712  &        1.220     &     4.076  &         0.000        &        2.581    &        7.361     \\\\\n",
       "\\textbf{C(strike)[9]}   &       9.7682  &        1.131     &     8.635  &         0.000        &        7.551    &       11.985     \\\\\n",
       "\\textbf{C(strike)[10]}  &      16.8084  &        1.094     &    15.367  &         0.000        &       14.665    &       18.952     \\\\\n",
       "\\textbf{C(strike)[11]}  &      12.9812  &        1.056     &    12.294  &         0.000        &       10.912    &       15.051     \\\\\n",
       "\\textbf{C(strike)[12]}  &      12.5542  &        1.380     &     9.099  &         0.000        &        9.850    &       15.258     \\\\\n",
       "\\textbf{C(strike)[14]}  &      20.8733  &        1.177     &    17.736  &         0.000        &       18.567    &       23.180     \\\\\n",
       "\\textbf{C(strike)[15]}  &      12.6472  &        0.657     &    19.258  &         0.000        &       11.360    &       13.934     \\\\\n",
       "\\textbf{C(strike)[17]}  &      21.7201  &        1.126     &    19.284  &         0.000        &       19.512    &       23.928     \\\\\n",
       "\\textbf{C(strike)[19]}  &      23.7198  &        1.001     &    23.703  &         0.000        &       21.758    &       25.681     \\\\\n",
       "\\textbf{C(strike)[20]}  &      26.8402  &        1.066     &    25.187  &         0.000        &       24.752    &       28.929     \\\\\n",
       "\\textbf{C(strike)[24]}  &      60.8126  &        1.173     &    51.824  &         0.000        &       58.513    &       63.112     \\\\\n",
       "\\textbf{C(strike)[26]}  &      29.4882  &        1.156     &    25.505  &         0.000        &       27.222    &       31.754     \\\\\n",
       "\\textbf{C(strike)[27]}  &      31.0061  &        1.138     &    27.247  &         0.000        &       28.776    &       33.236     \\\\\n",
       "\\textbf{C(strike)[28]}  &      21.5704  &        1.062     &    20.305  &         0.000        &       19.488    &       23.652     \\\\\n",
       "\\textbf{C(strike)[31]}  &      11.9036  &        1.280     &     9.302  &         0.000        &        9.395    &       14.412     \\\\\n",
       "\\textbf{C(strike)[32]}  &      20.0888  &        1.227     &    16.367  &         0.000        &       17.683    &       22.494     \\\\\n",
       "\\textbf{C(strike)[33]}  &      14.5246  &        1.206     &    12.041  &         0.000        &       12.160    &       16.889     \\\\\n",
       "\\textbf{C(strike)[34]}  &      12.4340  &        1.210     &    10.275  &         0.000        &       10.062    &       14.806     \\\\\n",
       "\\textbf{C(strike)[35]}  &      11.3830  &        1.187     &     9.591  &         0.000        &        9.057    &       13.709     \\\\\n",
       "\\textbf{C(strike)[36]}  &       9.8922  &        1.207     &     8.197  &         0.000        &        7.527    &       12.257     \\\\\n",
       "\\textbf{C(strike)[37]}  &       7.3523  &        1.211     &     6.072  &         0.000        &        4.979    &        9.725     \\\\\n",
       "\\textbf{C(strike)[38]}  &       8.6382  &        1.145     &     7.545  &         0.000        &        6.394    &       10.882     \\\\\n",
       "\\textbf{C(strike)[39]}  &       9.0848  &        1.195     &     7.601  &         0.000        &        6.742    &       11.427     \\\\\n",
       "\\textbf{C(strike)[40]}  &      29.1468  &        1.241     &    23.486  &         0.000        &       26.714    &       31.579     \\\\\n",
       "\\textbf{C(strike)[41]}  &      10.3589  &        1.209     &     8.567  &         0.000        &        7.989    &       12.729     \\\\\n",
       "\\textbf{C(strike)[42]}  &      19.8789  &        1.220     &    16.295  &         0.000        &       17.488    &       22.270     \\\\\n",
       "\\textbf{C(strike)[43]}  &      21.5096  &        1.097     &    19.615  &         0.000        &       19.360    &       23.659     \\\\\n",
       "\\textbf{C(strike)[45]}  &      96.9802  &        1.100     &    88.135  &         0.000        &       94.824    &       99.137     \\\\\n",
       "\\textbf{C(strike)[47]}  &      14.4997  &        1.204     &    12.041  &         0.000        &       12.140    &       16.860     \\\\\n",
       "\\textbf{C(strike)[48]}  &      24.5348  &        1.197     &    20.492  &         0.000        &       22.188    &       26.881     \\\\\n",
       "\\textbf{C(strike)[49]}  &      17.3984  &        1.208     &    14.404  &         0.000        &       15.031    &       19.766     \\\\\n",
       "\\textbf{C(strike)[50]}  &      15.0687  &        1.229     &    12.257  &         0.000        &       12.659    &       17.478     \\\\\n",
       "\\textbf{C(strike)[51]}  &      61.0146  &        1.174     &    51.988  &         0.000        &       58.714    &       63.315     \\\\\n",
       "\\textbf{C(strike)[54]}  &      21.4804  &        1.228     &    17.489  &         0.000        &       19.073    &       23.888     \\\\\n",
       "\\textbf{C(strike)[56]}  &      28.5644  &        1.130     &    25.268  &         0.000        &       26.349    &       30.780     \\\\\n",
       "\\textbf{C(strike)[57]}  &      16.3145  &        1.222     &    13.347  &         0.000        &       13.919    &       18.710     \\\\\n",
       "\\textbf{C(strike)[58]}  &      25.1248  &        1.102     &    22.802  &         0.000        &       22.965    &       27.284     \\\\\n",
       "\\textbf{C(strike)[59]}  &      17.7119  &        1.239     &    14.294  &         0.000        &       15.283    &       20.140     \\\\\n",
       "\\textbf{C(strike)[60]}  &      23.2158  &        1.069     &    21.708  &         0.000        &       21.120    &       25.312     \\\\\n",
       "\\textbf{C(strike)[61]}  &      25.0987  &        1.174     &    21.382  &         0.000        &       22.798    &       27.399     \\\\\n",
       "\\textbf{C(strike)[62]}  &      44.3749  &        1.188     &    37.367  &         0.000        &       42.047    &       46.702     \\\\\n",
       "\\textbf{C(strike)[64]}  &      21.9249  &        1.195     &    18.354  &         0.000        &       19.584    &       24.266     \\\\\n",
       "\\textbf{C(strike)[65]}  &      60.4603  &        1.166     &    51.863  &         0.000        &       58.175    &       62.745     \\\\\n",
       "\\textbf{C(strike)[67]}  &      19.7693  &        1.068     &    18.509  &         0.000        &       17.676    &       21.863     \\\\\n",
       "\\textbf{C(strike)[68]}  &      23.4975  &        1.102     &    21.322  &         0.000        &       21.338    &       25.657     \\\\\n",
       "\\textbf{C(strike)[69]}  &      21.3928  &        1.071     &    19.971  &         0.000        &       19.293    &       23.492     \\\\\n",
       "\\textbf{C(strike)[70]}  &      39.6671  &        1.180     &    33.608  &         0.000        &       37.354    &       41.980     \\\\\n",
       "\\textbf{C(strike)[71]}  &      70.0063  &        1.150     &    60.868  &         0.000        &       67.752    &       72.260     \\\\\n",
       "\\textbf{C(strike)[72]}  &      15.5273  &        1.228     &    12.647  &         0.000        &       13.121    &       17.934     \\\\\n",
       "\\textbf{C(strike)[75]}  &       5.8710  &        1.196     &     4.907  &         0.000        &        3.526    &        8.216     \\\\\n",
       "\\textbf{C(strike)[76]}  &      33.4700  &        1.155     &    28.971  &         0.000        &       31.206    &       35.734     \\\\\n",
       "\\textbf{C(strike)[77]}  &      26.4433  &        1.225     &    21.579  &         0.000        &       24.042    &       28.845     \\\\\n",
       "\\textbf{C(strike)[78]}  &      22.3530  &        1.186     &    18.854  &         0.000        &       20.029    &       24.677     \\\\\n",
       "\\textbf{C(strike)[81]}  &      34.7242  &        1.168     &    29.740  &         0.000        &       32.436    &       37.013     \\\\\n",
       "\\textbf{C(strike)[82]}  &      38.1832  &        1.071     &    35.655  &         0.000        &       36.084    &       40.282     \\\\\n",
       "\\textbf{C(strike)[83]}  &      27.2388  &        1.042     &    26.135  &         0.000        &       25.196    &       29.282     \\\\\n",
       "\\textbf{C(strike)[85]}  &      36.4337  &        1.081     &    33.694  &         0.000        &       34.314    &       38.553     \\\\\n",
       "\\textbf{C(strike)[87]}  &       9.8531  &        0.925     &    10.657  &         0.000        &        8.041    &       11.665     \\\\\n",
       "\\textbf{C(strike)[90]}  &       5.7953  &        1.296     &     4.473  &         0.000        &        3.256    &        8.335     \\\\\n",
       "\\textbf{C(strike)[92]}  &      20.7836  &        1.140     &    18.224  &         0.000        &       18.548    &       23.019     \\\\\n",
       "\\textbf{C(strike)[95]}  &      62.0763  &        0.824     &    75.292  &         0.000        &       60.460    &       63.692     \\\\\n",
       "\\textbf{C(strike)[96]}  &       4.7274  &        1.217     &     3.884  &         0.000        &        2.342    &        7.113     \\\\\n",
       "\\textbf{C(strike)[97]}  &      29.0708  &        1.255     &    23.172  &         0.000        &       26.612    &       31.530     \\\\\n",
       "\\textbf{C(strike)[100]} &      93.1699  &        1.146     &    81.317  &         0.000        &       90.924    &       95.416     \\\\\n",
       "\\textbf{C(strike)[101]} &       5.9158  &        1.240     &     4.771  &         0.000        &        3.486    &        8.346     \\\\\n",
       "\\textbf{C(strike)[102]} &      25.7969  &        1.246     &    20.699  &         0.000        &       23.354    &       28.240     \\\\\n",
       "\\textbf{C(strike)[106]} &       7.9504  &        0.541     &    14.706  &         0.000        &        6.891    &        9.010     \\\\\n",
       "\\textbf{C(strike)[107]} &      51.1471  &        0.497     &   102.822  &         0.000        &       50.172    &       52.122     \\\\\n",
       "\\textbf{X\\_1}           &      -1.1016  &        0.968     &    -1.137  &         0.255        &       -3.000    &        0.797     \\\\\n",
       "\\textbf{X\\_2}           &      -0.1545  &        0.739     &    -0.209  &         0.834        &       -1.603    &        1.294     \\\\\n",
       "\\textbf{X\\_3}           &      -0.8605  &        0.852     &    -1.010  &         0.313        &       -2.531    &        0.810     \\\\\n",
       "\\textbf{X\\_4}           &      -0.9970  &        0.811     &    -1.229  &         0.219        &       -2.586    &        0.592     \\\\\n",
       "\\textbf{X\\_5}           &      -0.4134  &        0.498     &    -0.830  &         0.407        &       -1.390    &        0.563     \\\\\n",
       "\\textbf{X\\_7}           &       0.4587  &        0.491     &     0.933  &         0.351        &       -0.505    &        1.422     \\\\\n",
       "\\textbf{X\\_8}           &      -0.3093  &        0.549     &    -0.563  &         0.573        &       -1.385    &        0.767     \\\\\n",
       "\\textbf{X\\_9}           &       2.2095  &        0.591     &     3.738  &         0.000        &        1.051    &        3.368     \\\\\n",
       "\\textbf{X\\_10}          &       2.8729  &        0.597     &     4.813  &         0.000        &        1.703    &        4.043     \\\\\n",
       "\\textbf{X\\_11}          &       4.0263  &        0.688     &     5.854  &         0.000        &        2.678    &        5.374     \\\\\n",
       "\\textbf{X\\_12}          &       4.3272  &        0.744     &     5.815  &         0.000        &        2.869    &        5.786     \\\\\n",
       "\\textbf{X\\_13}          &       4.6477  &        0.805     &     5.774  &         0.000        &        3.070    &        6.225     \\\\\n",
       "\\textbf{X\\_14}          &       5.7866  &        0.872     &     6.636  &         0.000        &        4.078    &        7.496     \\\\\n",
       "\\textbf{X\\_15}          &       5.5172  &        0.885     &     6.235  &         0.000        &        3.783    &        7.252     \\\\\n",
       "\\textbf{X\\_16}          &       5.9723  &        0.855     &     6.987  &         0.000        &        4.297    &        7.648     \\\\\n",
       "\\textbf{X\\_17}          &       7.3376  &        1.186     &     6.184  &         0.000        &        5.012    &        9.663     \\\\\n",
       "\\textbf{X\\_18}          &       6.9652  &        1.087     &     6.405  &         0.000        &        4.834    &        9.097     \\\\\n",
       "\\textbf{X\\_19}          &       6.3606  &        0.920     &     6.916  &         0.000        &        4.558    &        8.163     \\\\\n",
       "\\textbf{X\\_20}          &       7.0390  &        1.093     &     6.437  &         0.000        &        4.896    &        9.182     \\\\\n",
       "\\textbf{X\\_21}          &       7.9148  &        1.053     &     7.517  &         0.000        &        5.851    &        9.979     \\\\\n",
       "\\textbf{X\\_22}          &       8.5625  &        1.051     &     8.150  &         0.000        &        6.503    &       10.622     \\\\\n",
       "\\textbf{X\\_23}          &       8.5001  &        1.079     &     7.881  &         0.000        &        6.386    &       10.614     \\\\\n",
       "\\textbf{X\\_24}          &       9.5089  &        1.342     &     7.088  &         0.000        &        6.879    &       12.138     \\\\\n",
       "\\textbf{X\\_25}          &       9.2133  &        1.219     &     7.561  &         0.000        &        6.825    &       11.602     \\\\\n",
       "\\textbf{X\\_26}          &       9.5344  &        1.356     &     7.030  &         0.000        &        6.876    &       12.193     \\\\\n",
       "\\textbf{X\\_27}          &       9.7210  &        1.407     &     6.909  &         0.000        &        6.963    &       12.479     \\\\\n",
       "\\textbf{X\\_28}          &      10.2070  &        1.278     &     7.988  &         0.000        &        7.703    &       12.711     \\\\\n",
       "\\textbf{X\\_29}          &      11.3109  &        1.334     &     8.480  &         0.000        &        8.697    &       13.925     \\\\\n",
       "\\textbf{X\\_30}          &      10.6988  &        1.271     &     8.417  &         0.000        &        8.207    &       13.190     \\\\\n",
       "\\textbf{X\\_31}          &      10.3884  &        1.304     &     7.966  &         0.000        &        7.833    &       12.944     \\\\\n",
       "\\textbf{X\\_32}          &      10.3415  &        1.463     &     7.070  &         0.000        &        7.475    &       13.208     \\\\\n",
       "\\textbf{X\\_33}          &      10.3768  &        1.408     &     7.370  &         0.000        &        7.617    &       13.136     \\\\\n",
       "\\textbf{X\\_34}          &      11.3573  &        1.612     &     7.046  &         0.000        &        8.198    &       14.516     \\\\\n",
       "\\textbf{X\\_35}          &      11.4947  &        1.520     &     7.563  &         0.000        &        8.516    &       14.474     \\\\\n",
       "\\textbf{X\\_36}          &      11.3062  &        1.465     &     7.717  &         0.000        &        8.434    &       14.178     \\\\\n",
       "\\textbf{X\\_37}          &      12.0474  &        1.672     &     7.205  &         0.000        &        8.770    &       15.325     \\\\\n",
       "\\textbf{X\\_38}          &      11.5768  &        1.716     &     6.746  &         0.000        &        8.213    &       14.940     \\\\\n",
       "\\textbf{X\\_39}          &      12.1792  &        1.810     &     6.730  &         0.000        &        8.632    &       15.726     \\\\\n",
       "\\textbf{X\\_40}          &      11.7583  &        2.011     &     5.848  &         0.000        &        7.817    &       15.699     \\\\\n",
       "\\textbf{X\\_41}          &      11.6675  &        1.868     &     6.245  &         0.000        &        8.006    &       15.329     \\\\\n",
       "\\textbf{X\\_42}          &      12.5009  &        1.954     &     6.399  &         0.000        &        8.672    &       16.330     \\\\\n",
       "\\textbf{X\\_43}          &      12.5784  &        1.830     &     6.875  &         0.000        &        8.993    &       16.164     \\\\\n",
       "\\textbf{X\\_44}          &      12.3761  &        1.697     &     7.291  &         0.000        &        9.049    &       15.703     \\\\\n",
       "\\textbf{X\\_45}          &      13.3908  &        2.027     &     6.607  &         0.000        &        9.418    &       17.363     \\\\\n",
       "\\textbf{X\\_46}          &      12.7666  &        1.892     &     6.746  &         0.000        &        9.057    &       16.476     \\\\\n",
       "\\textbf{X\\_47}          &      13.3787  &        2.078     &     6.437  &         0.000        &        9.305    &       17.452     \\\\\n",
       "\\textbf{X\\_48}          &      12.8671  &        2.045     &     6.293  &         0.000        &        8.860    &       16.874     \\\\\n",
       "\\textbf{X\\_49}          &      13.4489  &        1.974     &     6.814  &         0.000        &        9.580    &       17.317     \\\\\n",
       "\\textbf{X\\_50}          &      13.1488  &        1.873     &     7.021  &         0.000        &        9.478    &       16.819     \\\\\n",
       "\\textbf{X\\_51}          &      13.4787  &        1.858     &     7.253  &         0.000        &        9.836    &       17.121     \\\\\n",
       "\\textbf{X\\_52}          &      13.4598  &        2.088     &     6.447  &         0.000        &        9.368    &       17.551     \\\\\n",
       "\\textbf{X\\_53}          &      13.1946  &        2.068     &     6.381  &         0.000        &        9.142    &       17.247     \\\\\n",
       "\\textbf{X\\_54}          &      12.8629  &        2.227     &     5.775  &         0.000        &        8.498    &       17.228     \\\\\n",
       "\\textbf{X\\_55}          &      13.1867  &        2.046     &     6.444  &         0.000        &        9.176    &       17.197     \\\\\n",
       "\\textbf{X\\_56}          &      13.4078  &        2.070     &     6.476  &         0.000        &        9.350    &       17.466     \\\\\n",
       "\\textbf{X\\_57}          &      12.8493  &        1.948     &     6.595  &         0.000        &        9.031    &       16.668     \\\\\n",
       "\\textbf{X\\_58}          &      13.0879  &        2.075     &     6.306  &         0.000        &        9.020    &       17.156     \\\\\n",
       "\\textbf{X\\_59}          &      12.8369  &        2.115     &     6.069  &         0.000        &        8.691    &       16.983     \\\\\n",
       "\\textbf{X\\_60}          &      12.8708  &        2.372     &     5.427  &         0.000        &        8.222    &       17.519     \\\\\n",
       "\\textbf{X\\_61}          &      11.4398  &        2.344     &     4.881  &         0.000        &        6.846    &       16.034     \\\\\n",
       "\\textbf{X\\_62}          &      12.8966  &        2.408     &     5.355  &         0.000        &        8.176    &       17.617     \\\\\n",
       "\\textbf{X\\_63}          &      12.7248  &        2.279     &     5.583  &         0.000        &        8.257    &       17.192     \\\\\n",
       "\\textbf{X\\_64}          &      12.7441  &        2.298     &     5.546  &         0.000        &        8.241    &       17.248     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 3143791.423 & \\textbf{  Durbin-Watson:     } &       1.975    \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 229762476.102  \\\\\n",
       "\\textbf{Skew:}          &     5.475   & \\textbf{  Prob(JB):          } &        0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &    44.832   & \\textbf{  Cond. No.          } &        35.9    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               distance   R-squared:                       0.029\n",
       "Model:                            OLS   Adj. R-squared:                  0.029\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 20 Aug 2025   Prob (F-statistic):                nan\n",
       "Time:                        13:55:34   Log-Likelihood:            -1.6456e+07\n",
       "No. Observations:             2949077   AIC:                         3.291e+07\n",
       "Df Residuals:                 2948940   BIC:                         3.291e+07\n",
       "Df Model:                         136                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[1]      16.0187      1.178     13.601      0.000      13.710      18.327\n",
       "C(strike)[3]      13.6911      1.207     11.343      0.000      11.325      16.057\n",
       "C(strike)[4]       9.0617      1.182      7.665      0.000       6.745      11.379\n",
       "C(strike)[5]      25.3413      1.225     20.683      0.000      22.940      27.743\n",
       "C(strike)[6]      25.2748      1.233     20.491      0.000      22.857      27.692\n",
       "C(strike)[7]      42.0044      1.146     36.643      0.000      39.758      44.251\n",
       "C(strike)[8]       4.9712      1.220      4.076      0.000       2.581       7.361\n",
       "C(strike)[9]       9.7682      1.131      8.635      0.000       7.551      11.985\n",
       "C(strike)[10]     16.8084      1.094     15.367      0.000      14.665      18.952\n",
       "C(strike)[11]     12.9812      1.056     12.294      0.000      10.912      15.051\n",
       "C(strike)[12]     12.5542      1.380      9.099      0.000       9.850      15.258\n",
       "C(strike)[14]     20.8733      1.177     17.736      0.000      18.567      23.180\n",
       "C(strike)[15]     12.6472      0.657     19.258      0.000      11.360      13.934\n",
       "C(strike)[17]     21.7201      1.126     19.284      0.000      19.512      23.928\n",
       "C(strike)[19]     23.7198      1.001     23.703      0.000      21.758      25.681\n",
       "C(strike)[20]     26.8402      1.066     25.187      0.000      24.752      28.929\n",
       "C(strike)[24]     60.8126      1.173     51.824      0.000      58.513      63.112\n",
       "C(strike)[26]     29.4882      1.156     25.505      0.000      27.222      31.754\n",
       "C(strike)[27]     31.0061      1.138     27.247      0.000      28.776      33.236\n",
       "C(strike)[28]     21.5704      1.062     20.305      0.000      19.488      23.652\n",
       "C(strike)[31]     11.9036      1.280      9.302      0.000       9.395      14.412\n",
       "C(strike)[32]     20.0888      1.227     16.367      0.000      17.683      22.494\n",
       "C(strike)[33]     14.5246      1.206     12.041      0.000      12.160      16.889\n",
       "C(strike)[34]     12.4340      1.210     10.275      0.000      10.062      14.806\n",
       "C(strike)[35]     11.3830      1.187      9.591      0.000       9.057      13.709\n",
       "C(strike)[36]      9.8922      1.207      8.197      0.000       7.527      12.257\n",
       "C(strike)[37]      7.3523      1.211      6.072      0.000       4.979       9.725\n",
       "C(strike)[38]      8.6382      1.145      7.545      0.000       6.394      10.882\n",
       "C(strike)[39]      9.0848      1.195      7.601      0.000       6.742      11.427\n",
       "C(strike)[40]     29.1468      1.241     23.486      0.000      26.714      31.579\n",
       "C(strike)[41]     10.3589      1.209      8.567      0.000       7.989      12.729\n",
       "C(strike)[42]     19.8789      1.220     16.295      0.000      17.488      22.270\n",
       "C(strike)[43]     21.5096      1.097     19.615      0.000      19.360      23.659\n",
       "C(strike)[45]     96.9802      1.100     88.135      0.000      94.824      99.137\n",
       "C(strike)[47]     14.4997      1.204     12.041      0.000      12.140      16.860\n",
       "C(strike)[48]     24.5348      1.197     20.492      0.000      22.188      26.881\n",
       "C(strike)[49]     17.3984      1.208     14.404      0.000      15.031      19.766\n",
       "C(strike)[50]     15.0687      1.229     12.257      0.000      12.659      17.478\n",
       "C(strike)[51]     61.0146      1.174     51.988      0.000      58.714      63.315\n",
       "C(strike)[54]     21.4804      1.228     17.489      0.000      19.073      23.888\n",
       "C(strike)[56]     28.5644      1.130     25.268      0.000      26.349      30.780\n",
       "C(strike)[57]     16.3145      1.222     13.347      0.000      13.919      18.710\n",
       "C(strike)[58]     25.1248      1.102     22.802      0.000      22.965      27.284\n",
       "C(strike)[59]     17.7119      1.239     14.294      0.000      15.283      20.140\n",
       "C(strike)[60]     23.2158      1.069     21.708      0.000      21.120      25.312\n",
       "C(strike)[61]     25.0987      1.174     21.382      0.000      22.798      27.399\n",
       "C(strike)[62]     44.3749      1.188     37.367      0.000      42.047      46.702\n",
       "C(strike)[64]     21.9249      1.195     18.354      0.000      19.584      24.266\n",
       "C(strike)[65]     60.4603      1.166     51.863      0.000      58.175      62.745\n",
       "C(strike)[67]     19.7693      1.068     18.509      0.000      17.676      21.863\n",
       "C(strike)[68]     23.4975      1.102     21.322      0.000      21.338      25.657\n",
       "C(strike)[69]     21.3928      1.071     19.971      0.000      19.293      23.492\n",
       "C(strike)[70]     39.6671      1.180     33.608      0.000      37.354      41.980\n",
       "C(strike)[71]     70.0063      1.150     60.868      0.000      67.752      72.260\n",
       "C(strike)[72]     15.5273      1.228     12.647      0.000      13.121      17.934\n",
       "C(strike)[75]      5.8710      1.196      4.907      0.000       3.526       8.216\n",
       "C(strike)[76]     33.4700      1.155     28.971      0.000      31.206      35.734\n",
       "C(strike)[77]     26.4433      1.225     21.579      0.000      24.042      28.845\n",
       "C(strike)[78]     22.3530      1.186     18.854      0.000      20.029      24.677\n",
       "C(strike)[81]     34.7242      1.168     29.740      0.000      32.436      37.013\n",
       "C(strike)[82]     38.1832      1.071     35.655      0.000      36.084      40.282\n",
       "C(strike)[83]     27.2388      1.042     26.135      0.000      25.196      29.282\n",
       "C(strike)[85]     36.4337      1.081     33.694      0.000      34.314      38.553\n",
       "C(strike)[87]      9.8531      0.925     10.657      0.000       8.041      11.665\n",
       "C(strike)[90]      5.7953      1.296      4.473      0.000       3.256       8.335\n",
       "C(strike)[92]     20.7836      1.140     18.224      0.000      18.548      23.019\n",
       "C(strike)[95]     62.0763      0.824     75.292      0.000      60.460      63.692\n",
       "C(strike)[96]      4.7274      1.217      3.884      0.000       2.342       7.113\n",
       "C(strike)[97]     29.0708      1.255     23.172      0.000      26.612      31.530\n",
       "C(strike)[100]    93.1699      1.146     81.317      0.000      90.924      95.416\n",
       "C(strike)[101]     5.9158      1.240      4.771      0.000       3.486       8.346\n",
       "C(strike)[102]    25.7969      1.246     20.699      0.000      23.354      28.240\n",
       "C(strike)[106]     7.9504      0.541     14.706      0.000       6.891       9.010\n",
       "C(strike)[107]    51.1471      0.497    102.822      0.000      50.172      52.122\n",
       "X_1               -1.1016      0.968     -1.137      0.255      -3.000       0.797\n",
       "X_2               -0.1545      0.739     -0.209      0.834      -1.603       1.294\n",
       "X_3               -0.8605      0.852     -1.010      0.313      -2.531       0.810\n",
       "X_4               -0.9970      0.811     -1.229      0.219      -2.586       0.592\n",
       "X_5               -0.4134      0.498     -0.830      0.407      -1.390       0.563\n",
       "X_7                0.4587      0.491      0.933      0.351      -0.505       1.422\n",
       "X_8               -0.3093      0.549     -0.563      0.573      -1.385       0.767\n",
       "X_9                2.2095      0.591      3.738      0.000       1.051       3.368\n",
       "X_10               2.8729      0.597      4.813      0.000       1.703       4.043\n",
       "X_11               4.0263      0.688      5.854      0.000       2.678       5.374\n",
       "X_12               4.3272      0.744      5.815      0.000       2.869       5.786\n",
       "X_13               4.6477      0.805      5.774      0.000       3.070       6.225\n",
       "X_14               5.7866      0.872      6.636      0.000       4.078       7.496\n",
       "X_15               5.5172      0.885      6.235      0.000       3.783       7.252\n",
       "X_16               5.9723      0.855      6.987      0.000       4.297       7.648\n",
       "X_17               7.3376      1.186      6.184      0.000       5.012       9.663\n",
       "X_18               6.9652      1.087      6.405      0.000       4.834       9.097\n",
       "X_19               6.3606      0.920      6.916      0.000       4.558       8.163\n",
       "X_20               7.0390      1.093      6.437      0.000       4.896       9.182\n",
       "X_21               7.9148      1.053      7.517      0.000       5.851       9.979\n",
       "X_22               8.5625      1.051      8.150      0.000       6.503      10.622\n",
       "X_23               8.5001      1.079      7.881      0.000       6.386      10.614\n",
       "X_24               9.5089      1.342      7.088      0.000       6.879      12.138\n",
       "X_25               9.2133      1.219      7.561      0.000       6.825      11.602\n",
       "X_26               9.5344      1.356      7.030      0.000       6.876      12.193\n",
       "X_27               9.7210      1.407      6.909      0.000       6.963      12.479\n",
       "X_28              10.2070      1.278      7.988      0.000       7.703      12.711\n",
       "X_29              11.3109      1.334      8.480      0.000       8.697      13.925\n",
       "X_30              10.6988      1.271      8.417      0.000       8.207      13.190\n",
       "X_31              10.3884      1.304      7.966      0.000       7.833      12.944\n",
       "X_32              10.3415      1.463      7.070      0.000       7.475      13.208\n",
       "X_33              10.3768      1.408      7.370      0.000       7.617      13.136\n",
       "X_34              11.3573      1.612      7.046      0.000       8.198      14.516\n",
       "X_35              11.4947      1.520      7.563      0.000       8.516      14.474\n",
       "X_36              11.3062      1.465      7.717      0.000       8.434      14.178\n",
       "X_37              12.0474      1.672      7.205      0.000       8.770      15.325\n",
       "X_38              11.5768      1.716      6.746      0.000       8.213      14.940\n",
       "X_39              12.1792      1.810      6.730      0.000       8.632      15.726\n",
       "X_40              11.7583      2.011      5.848      0.000       7.817      15.699\n",
       "X_41              11.6675      1.868      6.245      0.000       8.006      15.329\n",
       "X_42              12.5009      1.954      6.399      0.000       8.672      16.330\n",
       "X_43              12.5784      1.830      6.875      0.000       8.993      16.164\n",
       "X_44              12.3761      1.697      7.291      0.000       9.049      15.703\n",
       "X_45              13.3908      2.027      6.607      0.000       9.418      17.363\n",
       "X_46              12.7666      1.892      6.746      0.000       9.057      16.476\n",
       "X_47              13.3787      2.078      6.437      0.000       9.305      17.452\n",
       "X_48              12.8671      2.045      6.293      0.000       8.860      16.874\n",
       "X_49              13.4489      1.974      6.814      0.000       9.580      17.317\n",
       "X_50              13.1488      1.873      7.021      0.000       9.478      16.819\n",
       "X_51              13.4787      1.858      7.253      0.000       9.836      17.121\n",
       "X_52              13.4598      2.088      6.447      0.000       9.368      17.551\n",
       "X_53              13.1946      2.068      6.381      0.000       9.142      17.247\n",
       "X_54              12.8629      2.227      5.775      0.000       8.498      17.228\n",
       "X_55              13.1867      2.046      6.444      0.000       9.176      17.197\n",
       "X_56              13.4078      2.070      6.476      0.000       9.350      17.466\n",
       "X_57              12.8493      1.948      6.595      0.000       9.031      16.668\n",
       "X_58              13.0879      2.075      6.306      0.000       9.020      17.156\n",
       "X_59              12.8369      2.115      6.069      0.000       8.691      16.983\n",
       "X_60              12.8708      2.372      5.427      0.000       8.222      17.519\n",
       "X_61              11.4398      2.344      4.881      0.000       6.846      16.034\n",
       "X_62              12.8966      2.408      5.355      0.000       8.176      17.617\n",
       "X_63              12.7248      2.279      5.583      0.000       8.257      17.192\n",
       "X_64              12.7441      2.298      5.546      0.000       8.241      17.248\n",
       "==============================================================================\n",
       "Omnibus:                  3143791.423   Durbin-Watson:                   1.975\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        229762476.102\n",
       "Skew:                           5.475   Prob(JB):                         0.00\n",
       "Kurtosis:                      44.832   Cond. No.                         35.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + ' + C(strike) - 1', \n",
    "             data=df).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df[['strike']])})\n",
    "\n",
    "filtered_vars = [v for v in reg.params.index if v.startswith('X_') and ('[T.True]' in v or '[False]' in v)]\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reindex the results to go from day = -7 to day = 56\n",
    "event_res = res[res.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res = event_res.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res['day'] = event_res['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>varname</th>\n",
       "      <th>params</th>\n",
       "      <th>ci_l</th>\n",
       "      <th>ci_h</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X_1</td>\n",
       "      <td>-1.101597</td>\n",
       "      <td>-2.999801</td>\n",
       "      <td>0.796608</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>X_2</td>\n",
       "      <td>-0.154481</td>\n",
       "      <td>-1.602752</td>\n",
       "      <td>1.293790</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>X_3</td>\n",
       "      <td>-0.860480</td>\n",
       "      <td>-2.531052</td>\n",
       "      <td>0.810092</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>X_4</td>\n",
       "      <td>-0.997031</td>\n",
       "      <td>-2.586474</td>\n",
       "      <td>0.592413</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>X_5</td>\n",
       "      <td>-0.413443</td>\n",
       "      <td>-1.389685</td>\n",
       "      <td>0.562799</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>X_60</td>\n",
       "      <td>12.870807</td>\n",
       "      <td>8.222278</td>\n",
       "      <td>17.519336</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>X_61</td>\n",
       "      <td>11.439752</td>\n",
       "      <td>6.845987</td>\n",
       "      <td>16.033516</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>X_62</td>\n",
       "      <td>12.896610</td>\n",
       "      <td>8.176334</td>\n",
       "      <td>17.616886</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>X_63</td>\n",
       "      <td>12.724846</td>\n",
       "      <td>8.257284</td>\n",
       "      <td>17.192408</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>X_64</td>\n",
       "      <td>12.744113</td>\n",
       "      <td>8.240537</td>\n",
       "      <td>17.247689</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   varname     params      ci_l       ci_h  day\n",
       "0      X_1  -1.101597 -2.999801   0.796608   -7\n",
       "1      X_2  -0.154481 -1.602752   1.293790   -6\n",
       "2      X_3  -0.860480 -2.531052   0.810092   -5\n",
       "3      X_4  -0.997031 -2.586474   0.592413   -4\n",
       "4      X_5  -0.413443 -1.389685   0.562799   -3\n",
       "..     ...        ...       ...        ...  ...\n",
       "58    X_60  12.870807  8.222278  17.519336   52\n",
       "59    X_61  11.439752  6.845987  16.033516   53\n",
       "60    X_62  12.896610  8.176334  17.616886   54\n",
       "61    X_63  12.724846  8.257284  17.192408   55\n",
       "62    X_64  12.744113  8.240537  17.247689   56\n",
       "\n",
       "[63 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance peaks 49 days after strikes\n",
      "Distance peaks at 13.48 km\n"
     ]
    }
   ],
   "source": [
    "print('Distance peaks %d days after strikes' %event_res.params.idxmax())\n",
    "print('Distance peaks at %.2f km' %event_res.params.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure 2b: average daily distance from strike region by proximal individuals around the 74 strikes\n",
    "# 95% confidence intervals are show in light blue\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.gca()\n",
    "\n",
    "\n",
    "ax.plot(event_res['day'], event_res['params'], color='C0', zorder=3)\n",
    "ax.fill_between(event_res['day'], event_res['ci_l'], event_res['ci_h'], \n",
    "                facecolor='C0', alpha=0.2, zorder=2)\n",
    "ax.axhline(y=0, color='k', alpha=0.7, linestyle='--', zorder=1)\n",
    "ax.axvline(x=0, color='k', alpha=0.3, linestyle='--', zorder=1)\n",
    "ax.set_xlim([-7,56])\n",
    "ax.set_ylim([-7,22])\n",
    "\n",
    "ax.set_ylabel('Distance (km)')\n",
    "ax.set_xlabel('Days since strike')\n",
    "fig = ax.get_figure()\n",
    "fig.set_size_inches(10, 6)\n",
    "fig.savefig('figures/figure2b.png', bbox_inches='tight', format='png', dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number and duration of individuals who flee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in mobility data recording the amount of time proximal individuals who live within the strike region\n",
    "# remain away from home after each strike\n",
    "duration = {}\n",
    "for idx, row in strikes.iterrows():\n",
    "    new_id = row['new_id']\n",
    "    duration[new_id] = pd.read_csv('data_mobility/duration_away_%s.csv' %str(new_id), index_col=0)['days']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for example, after a strike on June 13, 2012 in Azzan, 30 individuals who live within the strike region\n",
    "# leave for at least 1 day. Several do not return within 29 days (after which we stop recording)\n",
    "duration[82].dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# report the number of proximal individuals who live within the strike region, leave within 24 hours of the strike,\n",
    "# and remain away for at least 24 hours (number who flee). also report the number of such individuals who do not\n",
    "# return within 29 days (after which we stop tracking, meaning these individuals effectively relocate).\n",
    "# lastly, report the percent of individuals who flee and return quickly within 5 days\n",
    "\n",
    "duration_all = pd.concat(duration)\n",
    "duration_all_dropna = duration_all.dropna()\n",
    "\n",
    "number_flee = len(duration_all.dropna())\n",
    "number_dont_return = (duration_all > 28).sum()\n",
    "percent_return_quickly = len(duration_all_dropna[duration_all_dropna < 5]) / float(len(duration_all_dropna))\n",
    "\n",
    "print('Number who flee: ', number_flee)\n",
    "print('Number who do not return: ', number_dont_return)\n",
    "print('Percent who return quickly: ', percent_return_quickly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# record the percent of proximal individuals who flee after all strikes\n",
    "\n",
    "percent = {}\n",
    "for idx, df in duration.items():\n",
    "    percent[idx] = len(df.dropna()) / len(df)\n",
    "percent_flee = pd.Series(percent).mean()\n",
    "\n",
    "print('Percent who flee: %.2f%%' %(percent_flee*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "drones",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
